From e42be32e4b264a6667a9c0605199efe72b16d9bc Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 13 Dec 2023 10:44:39 -0700 Subject: [PATCH 1/2] website for v0.7.0 --- docs/CONTRIBUTING.html | 162 +++++++++++++ docs/articles/cmdstanr-internals.html | 156 ++++++------ docs/articles/cmdstanr.html | 77 +++--- docs/articles/posterior.html | 204 ++++++++-------- docs/articles/profiling.html | 20 +- docs/index.html | 10 +- docs/news/index.html | 2 +- docs/pkgdown.yml | 2 +- docs/reference/CmdStanDiagnose.html | 8 +- docs/reference/CmdStanGQ.html | 2 +- docs/reference/CmdStanModel.html | 67 +++++- docs/reference/Rplot001.png | Bin 5039 -> 1011 bytes docs/reference/as_draws.CmdStanMCMC.html | 115 ++++----- docs/reference/cmdstan_model.html | 67 +++++- docs/reference/cmdstanr-package.html | 69 +++++- docs/reference/cmdstanr_example.html | 123 +++++----- docs/reference/draws_to_csv.html | 8 +- .../reference/fit-method-cmdstan_summary.html | 226 +++++++++--------- .../fit-method-diagnostic_summary.html | 17 +- docs/reference/fit-method-draws-1.png | Bin 59359 -> 59705 bytes docs/reference/fit-method-draws-2.png | Bin 641686 -> 668060 bytes docs/reference/fit-method-draws.html | 118 ++++----- docs/reference/fit-method-grad_log_prob.html | 17 +- docs/reference/fit-method-gradients.html | 10 +- docs/reference/fit-method-hessian.html | 13 +- docs/reference/fit-method-init.html | 8 +- docs/reference/fit-method-inv_metric.html | 82 +++---- docs/reference/fit-method-log_prob.html | 19 +- docs/reference/fit-method-loo.html | 4 +- docs/reference/fit-method-lp-1.png | Bin 104052 -> 91356 bytes docs/reference/fit-method-lp.html | 2 +- docs/reference/fit-method-metadata.html | 26 +- docs/reference/fit-method-mle.html | 8 +- docs/reference/fit-method-output.html | 48 ++-- docs/reference/fit-method-profiles.html | 16 +- docs/reference/fit-method-return_codes.html | 4 +- .../fit-method-sampler_diagnostics.html | 42 ++-- docs/reference/fit-method-save_object.html | 20 +- .../fit-method-save_output_files.html | 34 +-- docs/reference/fit-method-summary.html | 88 +++---- docs/reference/fit-method-time.html | 16 +- docs/reference/index.html | 2 +- docs/reference/model-method-check_syntax.html | 4 +- docs/reference/model-method-compile.html | 4 +- docs/reference/model-method-diagnose.html | 18 +- docs/reference/model-method-format.html | 2 +- docs/reference/model-method-laplace-1.png | Bin 12841 -> 13199 bytes docs/reference/model-method-laplace.html | 32 +-- docs/reference/model-method-optimize.html | 71 +++++- docs/reference/model-method-pathfinder.html | 110 ++++++--- docs/reference/model-method-sample.html | 75 ++++-- docs/reference/model-method-sample_mpi.html | 16 +- docs/reference/model-method-variational.html | 72 +++++- docs/reference/read_cmdstan_csv.html | 58 ++--- docs/reference/write_stan_file.html | 2 +- docs/reference/write_stan_json.html | 12 +- docs/sitemap.xml | 3 + 57 files changed, 1455 insertions(+), 936 deletions(-) create mode 100644 docs/CONTRIBUTING.html diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html new file mode 100644 index 000000000..ce8b14b16 --- /dev/null +++ b/docs/CONTRIBUTING.html @@ -0,0 +1,162 @@ + +Contributing to cmdstanr • cmdstanr + + +
+
+ + + +
+
+
+

Contributing to cmdstanr

+
+ +
+ +

This outlines how to propose a change to cmdstanr and is based on similar instructions for tidyverse packages, including the contributing guidelines generated by usethis::use_tidy_contributing().

+
+

Fixing typos

+

You can fix typos, spelling mistakes, or grammatical errors in the documentation directly using the GitHub web interface, as long as the changes are made in the source file. This generally means you’ll need to edit roxygen2 comments in an .R, not a .Rd file. You can find the .R file that generates the .Rd by reading the comment in the first line.

+
+
+

Bigger changes

+

If you want to make a bigger change, it’s a good idea to first file an issue and make sure someone from the team agrees that it’s needed. If you’ve found a bug, please file an issue that illustrates the bug with a minimal reproducible example (see e.g. the tidyverse reprex instructions). The tidyverse guide on how to create a great issue has more advice.

+
+

Pull request process

+

If you are new to creating pull requests here are some tips. Using the functions from the usethis package is not required but can be helpful if this process is new to you.

+

  • Fork the package and clone onto your computer. If you haven’t done this before, we recommend using usethis::create_from_github("stan-dev/cmdstanr", fork = TRUE).

    • +

  • Install all development dependencies with devtools::install_dev_deps(), and then make sure the package passes R CMD check by running devtools::check(). If R CMD check doesn’t pass cleanly, it’s a good idea to ask for help before continuing.

    • +

  • Create a Git branch for your pull request (PR). We recommend using usethis::pr_init("brief-description-of-change").

    • +

  • Make your changes, commit to git, and then create a PR by running usethis::pr_push(), and following the prompts in your browser. The title of your PR should briefly describe the change. The body of your PR should contain Fixes #issue-number.

    • +

  • For user-facing changes, add a bullet to the top of NEWS.md (i.e. just below the first header). Follow the style already used in NEWS.md.

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+

Code style

+

  • New code should attempt to follow the style used in the package. When in doubt follow the tidyverse style guide.

    • +

  • We use roxygen2, with Markdown syntax, for documentation.

    • +

  • We use testthat for unit tests. Contributions with test cases included are easier to accept.

    • +
+
+
+

Code of Conduct

+

Please note that the cmdstanr project follows the Stan project’s Code of Conduct. By contributing to this project you agree to abide by its terms.

+
+
+ +
+ + + +
+ + + +
+

Developed by Jonah Gabry, Rok Češnovar, Andrew Johnson.

+
+ +
+

Site built with pkgdown 2.0.7.

+
+ +
+ + + + + + + + diff --git a/docs/articles/cmdstanr-internals.html b/docs/articles/cmdstanr-internals.html index 1e55d1e06..cc89ad19c 100644 --- a/docs/articles/cmdstanr-internals.html +++ b/docs/articles/cmdstanr-internals.html @@ -250,7 +250,7 @@

Pedantic check

For the latest information on the checks performed in pedantic mode -see the Pedantic +see the Pedantic mode chapter in the Stan Reference Manual.

Pedantic mode is available when compiling the model or when using the separate $check_syntax() method of a @@ -278,7 +278,7 @@

Pedantic check$compile() method directly if using the delayed compilation approach described above).

mod_pedantic <- cmdstan_model(stan_file_pedantic, pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d863015ed4.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-74281eb75594.stan', line 11, column 14: A
     poisson distribution is given parameter lambda as a rate parameter
     (argument 1), but lambda was not constrained to be strictly positive.
 Warning: The parameter lambda has no priors. This means either no prior is
@@ -288,7 +288,7 @@ 
Pedantic checkpedantic argument to the $check_syntax()
 method.

 
mod_pedantic$check_syntax(pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
     poisson distribution is given parameter lambda as a rate parameter
     (argument 1), but lambda was not constrained to be strictly positive.
 Warning: The parameter lambda has no priors. This means either no prior is
@@ -305,7 +305,7 @@ 
Pedantic check
 mod_pedantic <- cmdstan_model(stan_file_pedantic, compile = FALSE)
 mod_pedantic$check_syntax(pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
     poisson distribution is given parameter lambda as a rate parameter
     (argument 1), but lambda was not constrained to be strictly positive.
 Warning: The parameter lambda has no priors. This means either no prior is
@@ -495,10 +495,10 @@ 
Default temporary files
 
 fit$output_files()

-
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-1-36f995.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-2-36f995.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-3-36f995.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-4-36f995.csv"

+
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-1-37e810.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-2-37e810.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-3-37e810.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-4-37e810.csv"

 
These files will be lost if you end your R session or if you remove
 the fit object and force (or wait for) garbage
 collection.

@@ -510,8 +510,8 @@ 
Default temporary filesrm(fit)
 gc()

 
          used (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
-Ncells 1249060 66.8    2424964 129.6         NA  2424964 129.6
-Vcells 2192048 16.8    8388608  64.0      32768  3407356  26.0

+Ncells 1260600 67.4    2436292 130.2         NA  2436292 130.2
+Vcells 2212085 16.9    8388608  64.0      32768  3459642  26.4
 file.exists(files)
[1] FALSE FALSE FALSE FALSE
@@ -561,14 +561,14 @@

Lazy CSV readingLazy CSV reading

After we call a method that requires the draws then if we reexamine @@ -623,14 +623,14 @@

Lazy CSV readingLazy CSV readingLazy CSV reading

For models with many parameters, transformed parameters, or generated @@ -688,7 +688,7 @@

read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()Saving and fit <- mod$sample(data = data_list, save_latent_dynamics = TRUE)

 fit$latent_dynamics_files()
-
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-1-460fca.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-2-460fca.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-3-460fca.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-4-460fca.csv"
+
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-1-425883.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-2-425883.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-3-425883.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-4-425883.csv"
 # read one of the files in
 x <- utils::read.csv(fit$latent_dynamics_files()[1], comment.char = "#")
 head(x)
      lp__ accept_stat__ stepsize__ treedepth__ n_leapfrog__ divergent__
-1 -6.84314      0.979774   0.874237           1            3           0
-2 -7.10483      0.971803   0.874237           2            3           0
-3 -6.85492      1.000000   0.874237           1            1           0
-4 -6.81455      0.951680   0.874237           2            3           0
-5 -6.90994      0.917527   0.874237           2            3           0
-6 -6.75277      1.000000   0.874237           1            3           0
-  energy__     theta   p_theta  g_theta
-1  6.84887 -0.814342  0.139388 0.683591
-2  7.10784 -0.558031 -0.101020 1.368040
-3  7.03534 -0.797632  0.782091 0.726389
-4  7.12956 -0.860038  1.033420 0.567976
-5  7.27609 -0.730035 -1.114150 0.902245
-6  6.87890 -1.033970 -0.653912 0.147785
+1 -9.73111 1.000000 0.932232 2 3 0 +2 -6.75346 1.000000 0.932232 1 3 0 +3 -8.48107 0.665681 0.932232 1 3 0 +4 -7.74234 1.000000 0.932232 1 1 0 +5 -6.82333 1.000000 0.932232 2 3 0 +6 -6.82333 0.784555 0.932232 1 3 0 + energy__ theta p_theta g_theta +1 10.97150 0.3939970 -2.155610 4.166930 +2 9.05119 -1.0295100 -2.933880 0.158161 +3 8.61284 0.0530292 0.702591 3.159050 +4 8.45793 -0.2148440 1.637290 2.357940 +5 7.76204 -1.3633100 1.875250 -0.555575 +6 8.04989 -1.3633100 -2.143570 -0.555575

The column lp__ is also provided via fit$draws(), and the columns accept_stat__, stepsize__, treedepth__, @@ -818,13 +818,13 @@

Saving and unique to the latent dynamics file.

 head(x[, c("theta", "p_theta", "g_theta")])
-
      theta   p_theta  g_theta
-1 -0.814342  0.139388 0.683591
-2 -0.558031 -0.101020 1.368040
-3 -0.797632  0.782091 0.726389
-4 -0.860038  1.033420 0.567976
-5 -0.730035 -1.114150 0.902245
-6 -1.033970 -0.653912 0.147785
+
       theta   p_theta   g_theta
+1  0.3939970 -2.155610  4.166930
+2 -1.0295100 -2.933880  0.158161
+3  0.0530292  0.702591  3.159050
+4 -0.2148440  1.637290  2.357940
+5 -1.3633100  1.875250 -0.555575
+6 -1.3633100 -2.143570 -0.555575

Our model has a single parameter theta and the three columns above correspond to theta in the unconstrained space (theta on the constrained @@ -872,16 +872,16 @@

Troubleshooting and debuggingmod <- cmdstan_model(stan_file, force_recompile = TRUE) 
Running make \
-  /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc \
+  /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467 \
   "STANCFLAGS +=  --name='bernoulli_model'"
 
 --- Translating Stan model to C++ code ---
-bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.stan
+bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.stan
 
 --- Compiling, linking C++ code ---
-clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS          -c -include-pch stan/src/stan/model/model_header_13_0.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.hpp
-clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS               -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb"        /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o src/cmdstan/main.o       -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb"     stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a  stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc
-rm -f /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o
+clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header_13_0.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.hpp +clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.o src/cmdstan/main.o -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467 +rm -f /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.o 
 fit <- mod$sample(
   data = data_list,
@@ -891,11 +891,11 @@ 
Troubleshooting and debugging)
Running MCMC with 1 chain...
 
-Running ./bernoulli 'id=1' random 'seed=2046085541' data \
-  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/standata-131d874013405.json' \
+Running ./bernoulli 'id=1' random 'seed=90958316' data \
+  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/standata-742874baeb02.json' \
   output \
-  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081036-1-2ef601.csv' \
-  'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-profile-202311081036-1-0c4891.csv' \
+  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-1-4db120.csv' \
+  'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-profile-202312131009-1-1c1f83.csv' \
   'method=sample' 'num_samples=100' 'num_warmup=100' 'save_warmup=0' \
   'algorithm=hmc' 'engine=nuts' adapt 'engaged=1'
 Chain 1 method = sample (Default) 
@@ -925,19 +925,19 @@ 
Troubleshooting and debuggingSampler diagnostic warnings a
 suffers from divergences.

 
-
Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
-See https://mc-stan.org/misc/warnings for details.

-
Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+
Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:

+
Chain 4 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmpl3ZPjU/model-76ac71f3c1fe.stan', line 14, column 2 to column 41)

+
Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,

+
Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.

+
Chain 4 

+
Warning: 149 of 4000 (4.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.

 
After fitting there is a warning about divergences. We can also
 regenerate this warning message later using
 fit$diagnostic_summary().

-
+
 diagnostics <- fit_with_warning$diagnostic_summary()

-
Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
-See https://mc-stan.org/misc/warnings for details.

-
Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+
Warning: 149 of 4000 (4.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.

-
+
 print(diagnostics)

 
$num_divergent
-[1]  3 12 97 12
+[1] 11 54 32 52
 
 $num_max_treedepth
 [1] 0 0 0 0
 
 $ebfmi
-[1] 0.20 0.29 0.29 0.43

-
+[1] 0.40 0.36 0.37 0.30

+
 # number of divergences reported in warning is the sum of the per chain values
 sum(diagnostics$num_divergent)

-
[1] 124

+
[1] 149

 

 

 
CmdStan’s diagnose utility
@@ -543,7 +544,7 @@ 
Create a stanfit object
+
 

 

@@ -561,26 +562,26 @@ 
Optimization$optimize().

-
+
 fit_mle <- mod$optimize(data = data_list, seed = 123)

 
Initial log joint probability = -9.51104 
     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
        6      -5.00402   0.000103557   2.55661e-07           1           1        9    
 Optimization terminated normally:  
   Convergence detected: relative gradient magnitude is below tolerance 
-Finished in  0.1 seconds.

-
+Finished in  0.2 seconds.

+
 fit_mle$print() # includes lp__ (log prob calculated by Stan program)

 
 variable estimate
     lp__     -5.00
     theta     0.20

-
+
 fit_mle$mle("theta")

 
theta 
   0.2 

 
Here’s a plot comparing the penalized MLE to the posterior
 distribution of theta.

-
+
 mcmc_hist(fit$draws("theta")) +
   vline_at(fit_mle$mle("theta"), size = 1.5)

 

@@ -591,7 +592,7 @@ 
OptimizationMaximum
 Likelihood Estimation section of the CmdStan User’s Guide for more
 details.

-
+
 fit_map <- mod$optimize(
   data = data_list,
   jacobian = TRUE,
@@ -620,7 +621,7 @@ 
Laplace Approximationmode argument. If mode is omitted then
 optimization will be run internally before taking draws from the normal
 approximation.

-
+
 fit_laplace <- mod$laplace(
     mode = fit_map,
     draws = 4000,
@@ -636,11 +637,11 @@ 
Laplace Approximation

-
+
 fit_laplace$print("theta")

 
 variable mean median   sd  mad   q5  q95
     theta 0.27   0.25 0.12 0.12 0.10 0.50

-
+
 mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025)

 

 

@@ -651,7 +652,7 @@ 
Variational (ADVI)$variational()
 method. For details on the ADVI algorithm see the CmdStan
 User’s Guide.

-
+
 fit_vb <- mod$variational(
   data = data_list,
   seed = 123,
@@ -662,8 +663,8 @@ 
Variational (ADVI)Variational (ADVI)
+
 fit_vb$print("theta")

 
 variable mean median   sd  mad   q5  q95
     theta 0.27   0.25 0.12 0.12 0.10 0.49

-
+
 mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025)

 

 

@@ -696,7 +697,7 @@ 
Variational (Pathfinder)CmdStan
 User’s Guide. Pathfinder is run using the $pathfinder()
 method.

-
+
 fit_pf <- mod$pathfinder(
   data = data_list,
   seed = 123,
@@ -720,7 +721,7 @@ 
Variational (Pathfinder)

-
+
 fit_pf$print("theta")

 
 variable mean median   sd  mad   q5  q95
     theta 0.25   0.24 0.12 0.12 0.08 0.47

@@ -728,22 +729,22 @@ 
Variational (Pathfinder)
-
+
 mcmc_hist(fit_pf$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Approximate posterior from pathfinder") +
   ggplot2::xlim(0, 1)

 

-
+
 mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Approximate posterior from variational") +
   ggplot2::xlim(0, 1)

 

-
+
 mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Approximate posterior from Laplace") +
   ggplot2::xlim(0, 1)

 

-
+
 mcmc_hist(fit$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Posterior from MCMC") +
   ggplot2::xlim(0, 1)

@@ -767,7 +768,7 @@ 
Saving fitted model objects
+
 fit$save_object(file = "fit.RDS")
 
 # can be read back in using readRDS
@@ -779,7 +780,7 @@ 
Saving fitted model objects$save_object()
 and replace saveRDS with the much faster
 qsave() function from the qs package.

-
+
 # Load CmdStan output files into the fitted model object.
 fit$draws() # Load posterior draws into the object.
 try(fit$sampler_diagnostics(), silent = TRUE) # Load sampler diagnostics.
@@ -795,7 +796,7 @@ 
Saving fitted model objects
+
 # Load posterior draws into the fitted model object and omit other output.
 fit$draws()
 
@@ -828,8 +829,8 @@ 
Advantages of RStanDeveloping
+CRAN. (Note: As of 2023, this can mostly be achieved with CmdStanR as
+well. See Developing
 using CmdStanR.)

 
  • Avoids use of R6 classes, which may result in more familiar
 syntax for many R users.
    • 
diff --git a/docs/articles/posterior.html b/docs/articles/posterior.html
index 783351c14..bf1156f81 100644
--- a/docs/articles/posterior.html
+++ b/docs/articles/posterior.html
@@ -147,80 +147,82 @@ 
    Summary statistics
     fit <- cmdstanr::cmdstanr_example("schools", method = "sample")
    
-
    Warning: 191 of 4000 (5.0%) transitions ended with a divergence.
+
    Warning: 411 of 4000 (10.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.
    
-
    +
    Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+See https://mc-stan.org/misc/warnings for details.
    
+
     fit$summary()
    
-
       variable       mean     median       sd      mad          q5       q95
-1      lp__ -57.917364 -58.126700 5.227350 5.348035 -66.0514450 -47.99920
-2        mu   6.421661   6.560445 3.994261 3.728717  -0.1207198  13.16044
-3       tau   5.211890   4.447485 3.457118 3.180740   1.0590100  12.09968
-4  theta[1]   9.159825   8.066620 6.731183 5.516199   0.1858543  21.43202
-5  theta[2]   6.750832   6.740980 5.320603 4.835678  -1.7502315  15.66325
-6  theta[3]   5.564623   5.963075 6.485297 5.310451  -5.2589090  15.65180
-7  theta[4]   6.506969   6.552735 5.753473 4.933870  -2.8831600  15.96813
-8  theta[5]   4.602639   4.969540 5.596007 4.792030  -5.1313190  13.08000
-9  theta[6]   5.451744   5.604770 5.781909 4.976021  -4.4380795  14.88214
-10 theta[7]   9.105948   8.421645 6.077003 5.363817   0.4464001  20.13298
-11 theta[8]   6.904986   6.666570 6.591653 5.244594  -3.4154830  18.00026
-       rhat   ess_bulk   ess_tail
-1  1.037320   87.01265   18.13542
-2  1.019548  741.86144 1129.22230
-3  1.041517   74.40670   16.64168
-4  1.012043  615.84853 1757.06290
-5  1.008696 1279.50483 2218.60618
-6  1.002604 1345.71326 1786.80529
-7  1.010044 1291.76655 1900.28317
-8  1.008882 1209.92612 1848.18547
-9  1.034876 1464.53969 1882.25434
-10 1.008780  554.66658 1685.21958
-11 1.034006 1102.79917 1784.94313
    
+
       variable       mean     median       sd      mad         q5       q95
+1      lp__ -55.581147 -56.536200 7.135791 7.809299 -66.108490 -44.17410
+2        mu   6.053657   5.516030 3.777403 3.597633  -0.119312  12.18247
+3       tau   4.456447   3.456865 3.641807 3.645828   0.778481  11.91315
+4  theta[1]   8.436941   7.287125 6.350694 4.628344   0.249281  20.38983
+5  theta[2]   6.522999   5.993620 5.308671 4.494072  -1.760367  15.64359
+6  theta[3]   5.147676   4.349235 6.014958 5.082679  -5.803791  14.17618
+7  theta[4]   6.189408   5.541310 5.578086 4.501730  -2.557880  15.63053
+8  theta[5]   4.669689   4.952550 5.338198 4.271882  -5.029484  12.96141
+9  theta[6]   5.123046   4.853110 5.623562 4.579796  -4.797084  13.70336
+10 theta[7]   8.283195   7.324155 5.703079 4.912514   1.112880  18.94623
+11 theta[8]   6.499894   5.594240 6.114406 4.839688  -2.747390  16.75831
+       rhat   ess_bulk    ess_tail
+1  1.348190   9.585696          NA
+2  1.094243 190.870204  910.104440
+3  1.305410  10.562073    4.833756
+4  1.037209 205.819798 1097.512010
+5  1.234963 615.265251 1158.714996
+6  1.270209 429.358164 1118.125260
+7  1.060915 288.566093 1331.398737
+8  1.265317 356.169676 1352.544578
+9  1.281493 482.412281 1311.140711
+10 1.041368  75.113061  938.699910
+11 1.069623 436.937773 1068.232198
    
 
    By default all variables are summaries with the follow functions:
    
-
    +
 
    [1] "mean"      "median"    "sd"        "mad"       "quantile2"
    
 
    To change the variables summarized, we use the variables argument
    
-
    +
     fit$summary(variables = c("mu", "tau"))
    
-
      variable     mean   median       sd      mad         q5      q95     rhat
-1       mu 6.421661 6.560445 3.994261 3.728717 -0.1207198 13.16044 1.019548
-2      tau 5.211890 4.447485 3.457118 3.180740  1.0590100 12.09968 1.041517
-  ess_bulk   ess_tail
-1 741.8614 1129.22230
-2  74.4067   16.64168
    
+
      variable     mean   median       sd      mad        q5      q95     rhat
+1       mu 6.053657 5.516030 3.777403 3.597633 -0.119312 12.18247 1.094243
+2      tau 4.456447 3.456865 3.641807 3.645828  0.778481 11.91315 1.305410
+   ess_bulk   ess_tail
+1 190.87020 910.104440
+2  10.56207   4.833756
    
 
    We can additionally change which functions are used
    
-
    +
     fit$summary(variables = c("mu", "tau"), mean, sd)
    
 
      variable     mean       sd
-1       mu 6.421661 3.994261
-2      tau 5.211890 3.457118
    
+1       mu 6.053657 3.777403
+2      tau 4.456447 3.641807
    
 
    To summarize all variables with non-default functions, it is
 necessary to set explicitly set the variables argument, either to
 NULL or the full vector of variable names.
    
-
    +
     fit$metadata()$model_params
    
 
     [1] "lp__"     "mu"       "tau"      "theta[1]" "theta[2]" "theta[3]"
  [7] "theta[4]" "theta[5]" "theta[6]" "theta[7]" "theta[8]"
    
-
    +
     fit$summary(variables = NULL, "mean", "median")
    
 
       variable       mean     median
-1      lp__ -57.917364 -58.126700
-2        mu   6.421661   6.560445
-3       tau   5.211890   4.447485
-4  theta[1]   9.159825   8.066620
-5  theta[2]   6.750832   6.740980
-6  theta[3]   5.564623   5.963075
-7  theta[4]   6.506969   6.552735
-8  theta[5]   4.602639   4.969540
-9  theta[6]   5.451744   5.604770
-10 theta[7]   9.105948   8.421645
-11 theta[8]   6.904986   6.666570
    
+1      lp__ -55.581147 -56.536200
+2        mu   6.053657   5.516030
+3       tau   4.456447   3.456865
+4  theta[1]   8.436941   7.287125
+5  theta[2]   6.522999   5.993620
+6  theta[3]   5.147676   4.349235
+7  theta[4]   6.189408   5.541310
+8  theta[5]   4.669689   4.952550
+9  theta[6]   5.123046   4.853110
+10 theta[7]   8.283195   7.324155
+11 theta[8]   6.499894   5.594240
    
 
    Summary functions can be specified by character string, function, or
 using a formula (or anything else supported by
 rlang::as_function()). If these arguments are named, those
 names will be used in the tibble output. If the summary results are
 named they will take precedence.
    
-
    +
     my_sd <- function(x) c(My_SD = sd(x))
 fit$summary(
   c("mu", "tau"), 
@@ -230,45 +232,45 @@ 
    Summary statistics~quantile(.x, probs = c(0.1, 0.9)),
   Minimum = function(x) min(x)
 )        
    
-
      variable     MEAN   median    My_SD      10%      90%    Minimum
-1       mu 6.421661 6.560445 3.994261 1.488318 11.54665 -15.442700
-2      tau 5.211890 4.447485 3.457118 1.488050 10.02499   0.822982
    
+
      variable     MEAN   median    My_SD      10%       90%   Minimum
+1       mu 6.053657 5.516030 3.777403 1.565533 10.863700 -8.554010
+2      tau 4.456447 3.456865 3.641807 0.778481  9.666494  0.774844
    
 
    Arguments to all summary functions can also be specified with
 .args.
    
-
    +
     fit$summary(c("mu", "tau"), quantile, .args = list(probs = c(0.025, .05, .95, .975)))
    
-
      variable      2.5%         5%      95%    97.5%
-1       mu -1.441798 -0.1207198 13.16044 14.63687
-2      tau  0.822982  1.0590100 12.09968 13.78430
    
+
      variable      2.5%        5%      95%    97.5%
+1       mu -1.614829 -0.119312 12.18247 13.54298
+2      tau  0.778481  0.778481 11.91315 13.43694
    
 
    The summary functions are applied to the array of sample values, with
 dimension iter_samplingxchains.
    
-
    +
     fit$summary(variables = NULL, dim, colMeans)
    
 
       variable dim.1 dim.2          1          2          3          4
-1      lp__  1000     4 -59.408601 -58.175641 -58.144473 -55.940741
-2        mu  1000     4   6.689529   6.327474   6.548562   6.121077
-3       tau  1000     4   6.015816   5.271415   5.222288   4.338039
-4  theta[1]  1000     4  10.309827   9.010128   9.089769   8.229576
-5  theta[2]  1000     4   6.940841   6.642238   6.917915   6.502333
-6  theta[3]  1000     4   5.378242   5.517793   5.881470   5.480988
-7  theta[4]  1000     4   6.732677   6.458448   6.658988   6.177763
-8  theta[5]  1000     4   4.460429   4.316417   4.822090   4.811620
-9  theta[6]  1000     4   5.567601   5.449356   5.743810   5.046207
-10 theta[7]  1000     4   9.872239   9.168575   9.143428   8.239549
-11 theta[8]  1000     4   6.985384   6.844136   7.146715   6.643711
    
+1      lp__  1000     4 -56.698590 -58.592226 -56.911658 -50.122112
+2        mu  1000     4   6.836467   6.338743   6.214918   4.824501
+3       tau  1000     4   4.751132   5.540196   4.617587   2.916872
+4  theta[1]  1000     4   9.139180   9.470688   8.631066   6.506831
+5  theta[2]  1000     4   7.088238   6.756565   6.691301   5.555891
+6  theta[3]  1000     4   6.087235   5.430155   5.208329   3.864985
+7  theta[4]  1000     4   6.963234   6.883517   6.322756   4.588127
+8  theta[5]  1000     4   5.312623   4.530775   4.623091   4.212267
+9  theta[6]  1000     4   5.778286   4.999767   5.497782   4.216348
+10 theta[7]  1000     4   9.354323   9.367796   8.483796   5.926863
+11 theta[8]  1000     4   7.269928   7.165762   6.670007   4.893880
    
 
    For this reason users may have unexpected results if they use
 stats::var() directly, as it will return a covariance
 matrix. An alternative is the distributional::variance()
 function, which can also be accessed via
 posterior::variance().
    
-
    +
     fit$summary(c("mu", "tau"), posterior::variance, ~var(as.vector(.x)))
    
 
      variable posterior::variance ~var(as.vector(.x))
-1       mu            15.95412            15.95412
-2      tau            11.95166            11.95166
    
+1       mu            14.26877            14.26877
+2      tau            13.26276            13.26276
    
 
    Summary functions need not be numeric, but these won’t work with
 $print().
    
-
    +
     strict_pos <- function(x) if (all(x > 0)) "yes" else "no"
 fit$summary(variables = NULL, "Strictly Positive" = strict_pos)
    
 
       variable Strictly Positive
@@ -283,7 +285,7 @@ 
    Summary statistics
    +
     # fit$print(variables = NULL, "Strictly Positive" = strict_pos)
    
 
    For more information, see posterior::summarise_draws(),
 which is called by $summary().
    
@@ -297,49 +299,49 @@ 
    Extracting posterior draws/samplesdraws_array and
 draws_df formats, but the posterior
 package supports other useful formats as well.
    
-
    +
-
     'draws_array' num [1:1000, 1:4, 1:11] -57.1 -58.5 -56 -57.2 -58.5 ...
+
     'draws_array' num [1:1000, 1:4, 1:11] -61.7 -60.5 -61.8 -60.5 -66.8 ...
  - attr(*, "dimnames")=List of 3
   ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
   ..$ chain    : chr [1:4] "1" "2" "3" "4"
   ..$ variable : chr [1:11] "lp__" "mu" "tau" "theta[1]" ...
    
-
    +
     # draws x variables data frame
 draws_df <- fit$draws(format = "df")
 str(draws_df)
    
 
    draws_df [4,000 × 14] (S3: draws_df/draws/tbl_df/tbl/data.frame)
- $ lp__      : num [1:4000] -57.1 -58.5 -56 -57.2 -58.5 ...
- $ mu        : num [1:4000] 9.17 4.62 11.15 5.67 5.37 ...
- $ tau       : num [1:4000] 2.8 4.94 2.62 5.14 7.06 ...
- $ theta[1]  : num [1:4000] 17.589 0.16 18.328 0.237 1.688 ...
- $ theta[2]  : num [1:4000] 8.55 10.22 8.37 9.18 9.6 ...
- $ theta[3]  : num [1:4000] 10.75 6.36 10.37 9.1 8.14 ...
- $ theta[4]  : num [1:4000] 10.85 3.4 12.92 6.16 6.3 ...
- $ theta[5]  : num [1:4000] 11 2.25 11.42 2.25 2.76 ...
- $ theta[6]  : num [1:4000] 9.55 8.19 9.94 6.84 4.9 ...
- $ theta[7]  : num [1:4000] 6.58 14.62 9.12 6.07 5.08 ...
- $ theta[8]  : num [1:4000] 11.31 6.14 10.26 4.92 7.66 ...
+ $ lp__      : num [1:4000] -61.7 -60.5 -61.8 -60.5 -66.8 ...
+ $ mu        : num [1:4000] 8.86 6.18 9.7 4.97 5.16 ...
+ $ tau       : num [1:4000] 5.6 8.77 7.58 7.37 11.97 ...
+ $ theta[1]  : num [1:4000] 16.716 14.062 6.67 6.25 -0.404 ...
+ $ theta[2]  : num [1:4000] 4.377 6.173 15.608 0.638 10.449 ...
+ $ theta[3]  : num [1:4000] 11.59 9.27 11.11 3.74 13.42 ...
+ $ theta[4]  : num [1:4000] 13.68 12.51 7.77 6.48 10.16 ...
+ $ theta[5]  : num [1:4000] -0.877 1.959 7.197 2.097 3.229 ...
+ $ theta[6]  : num [1:4000] -5.02 -4.95 5.66 5.24 14.26 ...
+ $ theta[7]  : num [1:4000] 13.38 17.44 13.69 6.65 13.77 ...
+ $ theta[8]  : num [1:4000] 10.46 5.48 -6.15 20.92 31.79 ...
  $ .chain    : int [1:4000] 1 1 1 1 1 1 1 1 1 1 ...
  $ .iteration: int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
  $ .draw     : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
    
-
    +
     print(draws_df)
    
 
    # A draws_df: 1000 iterations, 4 chains, and 11 variables
-   lp__    mu tau theta[1] theta[2] theta[3] theta[4] theta[5]
-1   -57  9.17 2.8    17.59      8.5    10.75     10.8     11.0
-2   -58  4.62 4.9     0.16     10.2     6.36      3.4      2.3
-3   -56 11.15 2.6    18.33      8.4    10.37     12.9     11.4
-4   -57  5.67 5.1     0.24      9.2     9.10      6.2      2.3
-5   -59  5.37 7.1     1.69      9.6     8.14      6.3      2.8
-6   -59  0.64 4.4     6.20      6.6     0.44      4.1      2.4
-7   -53  7.85 3.0     6.58      6.7     8.34      7.4      6.7
-8   -54  8.05 3.9     7.66      6.5     4.54      7.1      8.3
-9   -54  6.80 3.3     7.75      8.5     1.37      6.6      9.4
-10  -59  5.09 5.8     0.40      9.4    -1.92     13.0      2.4
+   lp__   mu  tau theta[1] theta[2] theta[3] theta[4] theta[5]
+1   -62  8.9  5.6     16.7     4.38     11.6   13.676    -0.88
+2   -61  6.2  8.8     14.1     6.17      9.3   12.515     1.96
+3   -62  9.7  7.6      6.7    15.61     11.1    7.770     7.20
+4   -60  5.0  7.4      6.3     0.64      3.7    6.477     2.10
+5   -67  5.2 12.0     -0.4    10.45     13.4   10.162     3.23
+6   -63 11.1  7.9     24.6    11.68     18.8   -1.606     3.79
+7   -61 14.7  6.1     28.9    11.15     15.3    6.780     9.95
+8   -63 13.4  5.7     13.7    21.03      7.2   20.051    12.99
+9   -64 16.0  8.3     20.2     2.65     11.3   19.000     3.51
+10  -59  2.9  4.5      4.2    11.37      2.5   -0.057     6.30
 # ... with 3990 more draws, and 3 more variables
 # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
    
 
    To convert an existing draws object to a different format use the
diff --git a/docs/articles/profiling.html b/docs/articles/profiling.html
index 613e6d866..dfbed9b18 100644
--- a/docs/articles/profiling.html
+++ b/docs/articles/profiling.html
@@ -250,11 +250,11 @@ 
    Accessing the profiling info
 fit$profiles()
    
 
    [[1]]
         name   thread_id total_time forward_time reverse_time chain_stack
-1 likelihood 0x10cc54e00 0.76348000   0.62696000   0.13652000       51132
-2     priors 0x10cc54e00 0.00513667   0.00296958   0.00216709       34088
+1 likelihood 0x118759e00 0.69970800   0.57529800   0.12441000       51468
+2     priors 0x118759e00 0.00446571   0.00270154   0.00176417       34312
   no_chain_stack autodiff_calls no_autodiff_calls
-1       34105044          17044                 1
-2          34088          17044                 1
    
+1       34329156          17156                 1
+2          34312          17156                 1
    
 
    The total_time column is the total time spent inside a
 given profile statement. It is clear that the vast majority of time is
 spent in the likelihood function.
    
@@ -297,11 +297,11 @@ 
    Comparing to a faster versio
 fit_glm$profiles()
    
 
    [[1]]
         name   thread_id total_time forward_time reverse_time chain_stack
-1 likelihood 0x115a87e00 0.39845200   0.39713500   0.00131726       49845
-2     priors 0x115a87e00 0.00381033   0.00234721   0.00146311       33230
+1 likelihood 0x106ce4e00 0.38400000   0.38264800   0.00135175       52203
+2     priors 0x106ce4e00 0.00356564   0.00237053   0.00119511       34802
   no_chain_stack autodiff_calls no_autodiff_calls
-1          16615          16615                 1
-2          33230          16615                 1
    
+1          17401          17401                 1
+2          34802          17401                 1
    
 
    We can see from the total_time column that this is much
 faster than the previous model.
    
 
    
@@ -324,7 +324,7 @@ 
    Per-gradient timings, and memory
 profile_chain_1 <- fit$profiles()[[1]]
 per_gradient_timing <- profile_chain_1$total_time/profile_chain_1$autodiff_calls
 print(per_gradient_timing) # two elements for the two profile statements in the model
    
-
    [1] 4.479465e-05 3.013770e-07
    
+
    [1] 4.078503e-05 2.603002e-07
    
 
    
 
    
 
    Accessing and saving the profile files
@@ -335,7 +335,7 @@ 
    Accessing and saving the profile
 $profile_files().
    
 
     fit$profile_files()
    
-
    [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI4doTK/model_6580008f67848265f3dfd0e7ae3b0600-profile-202311081036-1-4a8018.csv"
    
+
    [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpJRtaoq/model_6580008f67848265f3dfd0e7ae3b0600-profile-202312131010-1-95e407.csv"
    
 
    These can be saved to a more permanent location with the
 $save_profile_files() method.
    
 
    diff --git a/docs/index.html b/docs/index.html
index cc3f76986..1e06b71ed 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -156,7 +156,7 @@ 
    Goals
 
  • A clean interface to Stan services so that CmdStanR can keep up with Stan releases.
    • 
 
  • R code that doesn’t interface directly with C++, only calls compiled executables.
    • 
 
  • Modularity: CmdStanR runs Stan’s algorithms and lets downstream modules do the analysis.
    • 
-
  • Flexible BSD-3 license.
    • 
+
  • Flexible BSD-3 license.
    • 
 
 
    
 
    
@@ -191,7 +191,7 @@ 
    License
 
    CmdStanR, like CmdStan and the core Stan C++ code, is licensed under the following licenses:
    
     
 
    
@@ -216,6 +216,12 @@ 
    License
    
 
 
    
 
+
    
+
    Community
    
+
+
    
 
 
    
 
    Citation
    
diff --git a/docs/news/index.html b/docs/news/index.html
index 64016e370..4dbb505ff 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -312,7 +312,7 @@ 
    Bug fixes
 
    New features
    
-
    • CSV reading is now faster by using data.table::fread(). (#318)
      • 
+
      • CSV reading is now faster by using data.table::fread(). (#318)
        • 
 
      • install_cmdstan() gains argument version for specifying which version of CmdStan to install. (#300, #308)
        • 
 
      • New function check_cmdstan_toolchain() that checks if the appropriate toolchains are available. (#289)
        • 
 
      • $sample() method for CmdStanModel objects gains argument chain_ids for specifying custom chain IDs. (#319)
        • 
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 2af8a222e..14ff6a178 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -8,7 +8,7 @@ articles:
   posterior: posterior.html
   profiling: profiling.html
   r-markdown: r-markdown.html
-last_built: 2023-11-08T17:25Z
+last_built: 2023-12-13T17:00Z
 urls:
   reference: https://mc-stan.org/cmdstanr/reference
   article: https://mc-stan.org/cmdstanr/articles
diff --git a/docs/reference/CmdStanDiagnose.html b/docs/reference/CmdStanDiagnose.html
index 18388338f..e0d66d56d 100644
--- a/docs/reference/CmdStanDiagnose.html
+++ b/docs/reference/CmdStanDiagnose.html
@@ -138,10 +138,10 @@ 
        Examples
        
 # retrieve the gradients
 test$gradients()
 #>   param_idx     value     model finite_diff        error
-#> 1         0  0.797265  -8.06234    -8.06234 -9.72013e-09
-#> 2         1  0.216729 -15.64540   -15.64540  1.12721e-08
-#> 3         2 -0.207005  -4.61097    -4.61097 -5.25634e-09
-#> 4         3  1.118110  -6.60883    -6.60883 -2.24051e-08
+#> 1         0 -1.501560  34.56550    34.56550  4.58586e-08
+#> 2         1 -1.199130   1.69981     1.69981 -1.08299e-09
+#> 3         2  1.255420 -23.66640   -23.66640 -2.23140e-08
+#> 4         3 -0.452023  12.80740    12.80740 -8.57077e-09
 # }
 
 
    
diff --git a/docs/reference/CmdStanGQ.html b/docs/reference/CmdStanGQ.html
index 28c7f7a4c..6506d9afc 100644
--- a/docs/reference/CmdStanGQ.html
+++ b/docs/reference/CmdStanGQ.html
@@ -177,7 +177,7 @@ 
    Examples
    
 #> 
 #> All 4 chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.6 seconds.
+#> Total execution time: 0.7 seconds.
 #> 
 
 # stan program for standalone generated quantities
diff --git a/docs/reference/CmdStanModel.html b/docs/reference/CmdStanModel.html
index a309a128d..6b2ba8d72 100644
--- a/docs/reference/CmdStanModel.html
+++ b/docs/reference/CmdStanModel.html
@@ -333,7 +333,7 @@ 
    Examples
    
 #>        6      -5.00402   0.000103557   2.55661e-07           1           1        9    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
-#> Finished in  0.1 seconds.
+#> Finished in  0.2 seconds.
 fit_optim$summary()
 #> # A tibble: 2 × 2
 #>   variable estimate
@@ -341,12 +341,12 @@ 
    Examples
    
 #> 1 lp__        -5.00
 #> 2 theta        0.2 
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -6.94684 
+#> Initial log joint probability = -6.92942 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        4      -6.74802   0.000319208   6.44211e-05      0.9206      0.9206        7    
+#>        4      -6.74802   0.000269426   5.11368e-05      0.9249      0.9249        7    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -377,11 +377,11 @@ 
    Examples
    
 #> Finished in  0.1 seconds.
 fit_laplace$summary()
 #> # A tibble: 3 × 7
-#>   variable      mean median    sd   mad     q5      q95
-#>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
-#> 1 lp__        -7.23  -6.96  0.692 0.298 -8.50  -6.75   
-#> 2 lp_approx__ -0.490 -0.226 0.691 0.313 -1.88  -0.00180
-#> 3 theta        0.267  0.249 0.122 0.119  0.102  0.497  
+#>   variable      mean median    sd   mad      q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
+#> 1 lp__        -7.22  -6.97  0.652 0.296 -8.51   -6.75   
+#> 2 lp_approx__ -0.492 -0.223 0.677 0.302 -1.91   -0.00222
+#> 3 theta        0.265  0.246 0.120 0.122  0.0952  0.484  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -450,6 +450,53 @@ 
    Examples
    
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -6.860971 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      7.999e-04   6.710e-06    1.000e+00  1.000e+00       101 -6.266e+00 -6.266e+00                   
+#> Path [1] :Best Iter: [2] ELBO (-6.159468) evaluations: (101) 
+#> Path [2] :Initial log joint density = -15.050783 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      2.035e-03   6.058e-05    1.000e+00  1.000e+00       126 -6.231e+00 -6.231e+00                   
+#> Path [2] :Best Iter: [2] ELBO (-6.179167) evaluations: (126) 
+#> Path [3] :Initial log joint density = -12.609181 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.519e-03   3.222e-05    1.000e+00  1.000e+00       126 -6.224e+00 -6.224e+00                   
+#> Path [3] :Best Iter: [3] ELBO (-6.177380) evaluations: (126) 
+#> Path [4] :Initial log joint density = -7.551395 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.242e-04   5.362e-07    1.000e+00  1.000e+00       126 -6.267e+00 -6.267e+00                   
+#> Path [4] :Best Iter: [4] ELBO (-6.163367) evaluations: (126) 
+#> Path [5] :Initial log joint density = -7.623161 
+#> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.410e-04   6.577e-07    1.000e+00  1.000e+00       126 -6.242e+00 -6.242e+00                   
+#> Path [5] :Best Iter: [5] ELBO (-6.241791) evaluations: (126) 
+#> Path [6] :Initial log joint density = -12.604877 
+#> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.517e-03   3.214e-05    1.000e+00  1.000e+00       126 -6.220e+00 -6.220e+00                   
+#> Path [6] :Best Iter: [4] ELBO (-6.150438) evaluations: (126) 
+#> Path [7] :Initial log joint density = -6.754804 
+#> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               3      -6.748e+00      1.079e-03   3.589e-05    1.000e+00  1.000e+00        76 -6.230e+00 -6.230e+00                   
+#> Path [7] :Best Iter: [2] ELBO (-6.228353) evaluations: (76) 
+#> Path [8] :Initial log joint density = -6.839688 
+#> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      6.089e-04   4.257e-06    1.000e+00  1.000e+00       101 -6.279e+00 -6.279e+00                   
+#> Path [8] :Best Iter: [3] ELBO (-6.230942) evaluations: (101) 
+#> Path [9] :Initial log joint density = -7.590990 
+#> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.335e-04   6.020e-07    1.000e+00  1.000e+00       126 -6.260e+00 -6.260e+00                   
+#> Path [9] :Best Iter: [4] ELBO (-6.210494) evaluations: (126) 
+#> Path [10] :Initial log joint density = -11.878141 
+#> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.194e-03   2.066e-05    1.000e+00  1.000e+00       126 -6.255e+00 -6.255e+00                   
+#> Path [10] :Best Iter: [2] ELBO (-6.222876) evaluations: (126) 
+#> Total log probability function evaluations:1310 
+#> Finished in  0.1 seconds.
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
@@ -516,7 +563,7 @@ 
    Examples
    
 #> 
 #> Both chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.4 seconds.
+#> Total execution time: 0.3 seconds.
 #> 
 fit_optim_w_init_list <- mod$optimize(
   data = stan_data,
diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png
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zw4^og3H4V;j}pj)e0HRt`Zz%zsFJ`AiLcaj_}_zzF>qYaJ>O-`7B$8^xJ6{+Z?{Lz8NFzdVy{C4JDGniAuwbRz!!jnHYzem|fWUxn0qkaTFAzeV=
zC)S6>xDss46}~0fQ2PF4@wz1w{Gj^*+Ww&L{t3L(Ss?%b

diff --git a/docs/reference/as_draws.CmdStanMCMC.html b/docs/reference/as_draws.CmdStanMCMC.html
index 40958e24a..ab9c18eca 100644
--- a/docs/reference/as_draws.CmdStanMCMC.html
+++ b/docs/reference/as_draws.CmdStanMCMC.html
@@ -158,41 +158,41 @@ 
    Examples
    
 #> 
 #>          chain
 #> iteration   1   2   3   4
-#>         1 -66 -68 -66 -65
-#>         2 -65 -67 -65 -66
-#>         3 -68 -66 -65 -66
-#>         4 -68 -65 -65 -67
-#>         5 -67 -64 -65 -67
+#>         1 -66 -67 -65 -67
+#>         2 -65 -65 -64 -67
+#>         3 -66 -66 -65 -64
+#>         4 -65 -65 -65 -66
+#>         5 -67 -67 -66 -65
 #> 
 #> , , variable = alpha
 #> 
 #>          chain
 #> iteration    1      2    3    4
-#>         1 0.44  0.414 0.71 0.46
-#>         2 0.30 -0.045 0.44 0.17
-#>         3 0.65  0.397 0.14 0.55
-#>         4 0.29  0.644 0.47 0.54
-#>         5 0.53  0.435 0.48 0.20
+#>         1 0.37 0.0232 0.30 0.19
+#>         2 0.35 0.1359 0.50 0.56
+#>         3 0.39 0.0021 0.22 0.42
+#>         4 0.42 0.2264 0.22 0.21
+#>         5 0.30 0.6367 0.19 0.24
 #> 
 #> , , variable = beta[1]
 #> 
 #>          chain
 #> iteration     1     2     3     4
-#>         1 -0.91 -0.17 -0.50 -0.59
-#>         2 -0.57 -0.97 -0.78 -0.65
-#>         3 -0.90 -0.28 -0.38 -0.43
-#>         4 -1.20 -0.58 -0.92 -1.04
-#>         5 -0.59 -0.74 -0.70 -0.98
+#>         1 -0.21 -0.70 -0.87 -0.93
+#>         2 -0.42 -0.81 -0.58 -0.38
+#>         3 -1.01 -0.83 -0.81 -0.49
+#>         4 -0.95 -0.45 -0.67 -1.03
+#>         5 -0.16 -0.96 -0.73 -0.89
 #> 
 #> , , variable = beta[2]
 #> 
 #>          chain
-#> iteration      1      2     3      4
-#>         1  0.088 -0.192 -0.43 -0.621
-#>         2 -0.515 -0.088 -0.35 -0.591
-#>         3 -0.031 -0.444 -0.11  0.017
-#>         4 -0.653 -0.229 -0.37  0.066
-#>         5 -0.345 -0.277 -0.13 -0.678
+#> iteration       1     2      3     4
+#>         1 -0.3841  0.08 -0.574 -0.73
+#>         2 -0.2843 -0.27 -0.376  0.12
+#>         3 -0.0223 -0.13 -0.065 -0.11
+#>         4  0.0016 -0.28 -0.100 -0.29
+#>         5 -0.5327 -0.66 -0.644 -0.16
 #> 
 #> # ... with 995 more iterations, and 101 more variables
 
@@ -200,70 +200,71 @@ 
    Examples
    
 posterior::as_draws_rvars(fit)
 #> # A draws_rvars: 1000 iterations, 4 chains, and 4 variables
 #> $lp__: rvar<1000,4>[1] mean ± sd:
-#> [1] -66 ± 1.5 
+#> [1] -66 ± 1.4 
 #> 
 #> $alpha: rvar<1000,4>[1] mean ± sd:
-#> [1] 0.38 ± 0.22 
+#> [1] 0.37 ± 0.22 
 #> 
 #> $beta: rvar<1000,4>[3] mean ± sd:
-#> [1] -0.67 ± 0.26  -0.27 ± 0.23   0.69 ± 0.27 
+#> [1] -0.66 ± 0.25  -0.27 ± 0.23   0.67 ± 0.26 
 #> 
 #> $log_lik: rvar<1000,4>[100] mean ± sd:
-#>   [1] -0.517 ± 0.098  -0.401 ± 0.152  -0.501 ± 0.223  -0.448 ± 0.154 
-#>   [5] -1.185 ± 0.289  -0.590 ± 0.195  -0.640 ± 0.126  -0.278 ± 0.135 
-#>   [9] -0.693 ± 0.170  -0.742 ± 0.238  -0.279 ± 0.127  -0.494 ± 0.244 
-#>  [13] -0.659 ± 0.215  -0.363 ± 0.176  -0.279 ± 0.108  -0.275 ± 0.087 
-#>  [17] -1.593 ± 0.288  -0.479 ± 0.107  -0.232 ± 0.076  -0.113 ± 0.080 
-#>  [21] -0.211 ± 0.089  -0.568 ± 0.149  -0.330 ± 0.140  -0.136 ± 0.067 
-#>  [25] -0.454 ± 0.123  -1.517 ± 0.342  -0.306 ± 0.123  -0.445 ± 0.083 
-#>  [29] -0.725 ± 0.232  -0.696 ± 0.191  -0.487 ± 0.164  -0.423 ± 0.109 
-#>  [33] -0.409 ± 0.130  -0.064 ± 0.052  -0.587 ± 0.189  -0.325 ± 0.132 
-#>  [37] -0.700 ± 0.231  -0.312 ± 0.151  -0.180 ± 0.111  -0.681 ± 0.128 
-#>  [41] -1.133 ± 0.258  -0.932 ± 0.200  -0.416 ± 0.265  -1.175 ± 0.185 
-#>  [45] -0.360 ± 0.118  -0.580 ± 0.131  -0.303 ± 0.131  -0.324 ± 0.082 
-#>  [49] -0.319 ± 0.079  -1.285 ± 0.333  -0.289 ± 0.094  -0.832 ± 0.142 
-#>  [53] -0.401 ± 0.131  -0.373 ± 0.143  -0.384 ± 0.139  -0.321 ± 0.192 
-#>  [57] -0.658 ± 0.119  -0.951 ± 0.363  -1.364 ± 0.348  -0.976 ± 0.158 
-#>  [61] -0.542 ± 0.097  -0.871 ± 0.318  -0.116 ± 0.075  -0.901 ± 0.250 
-#>  [65] -2.026 ± 0.610  -0.508 ± 0.138  -0.276 ± 0.082  -1.061 ± 0.237 
-#>  [69] -0.435 ± 0.083  -0.641 ± 0.235  -0.613 ± 0.214  -0.464 ± 0.177 
-#>  [73] -1.489 ± 0.374  -0.948 ± 0.198  -1.136 ± 0.394  -0.374 ± 0.140 
-#>  [77] -0.878 ± 0.139  -0.491 ± 0.174  -0.765 ± 0.194  -0.539 ± 0.200 
-#>  [81] -0.163 ± 0.102  -0.223 ± 0.145  -0.343 ± 0.080  -0.275 ± 0.092 
-#>  [85] -0.130 ± 0.075  -1.132 ± 0.322  -0.822 ± 0.126  -0.780 ± 0.249 
-#>  [89] -1.283 ± 0.323  -0.261 ± 0.137  -0.387 ± 0.133  -1.501 ± 0.352 
-#>  [93] -0.737 ± 0.221  -0.318 ± 0.087  -0.388 ± 0.112  -1.573 ± 0.284 
-#>  [97] -0.432 ± 0.100  -1.053 ± 0.383  -0.693 ± 0.143  -0.392 ± 0.096 
+#>   [1] -0.518 ± 0.099  -0.405 ± 0.147  -0.501 ± 0.220  -0.451 ± 0.153 
+#>   [5] -1.173 ± 0.276  -0.592 ± 0.185  -0.640 ± 0.123  -0.280 ± 0.135 
+#>   [9] -0.693 ± 0.163  -0.740 ± 0.234  -0.283 ± 0.124  -0.500 ± 0.241 
+#>  [13] -0.655 ± 0.206  -0.364 ± 0.175  -0.282 ± 0.106  -0.278 ± 0.087 
+#>  [17] -1.582 ± 0.288  -0.481 ± 0.107  -0.235 ± 0.076  -0.115 ± 0.079 
+#>  [21] -0.215 ± 0.088  -0.570 ± 0.144  -0.332 ± 0.140  -0.139 ± 0.067 
+#>  [25] -0.456 ± 0.121  -1.513 ± 0.345  -0.309 ± 0.120  -0.447 ± 0.083 
+#>  [29] -0.722 ± 0.227  -0.694 ± 0.189  -0.489 ± 0.157  -0.427 ± 0.106 
+#>  [33] -0.412 ± 0.126  -0.065 ± 0.052  -0.585 ± 0.184  -0.327 ± 0.135 
+#>  [37] -0.700 ± 0.227  -0.314 ± 0.150  -0.182 ± 0.110  -0.681 ± 0.126 
+#>  [41] -1.124 ± 0.248  -0.928 ± 0.195  -0.412 ± 0.267  -1.169 ± 0.185 
+#>  [45] -0.362 ± 0.119  -0.583 ± 0.128  -0.307 ± 0.129  -0.327 ± 0.084 
+#>  [49] -0.322 ± 0.079  -1.286 ± 0.334  -0.291 ± 0.096  -0.833 ± 0.144 
+#>  [53] -0.404 ± 0.127  -0.373 ± 0.143  -0.388 ± 0.134  -0.322 ± 0.195 
+#>  [57] -0.658 ± 0.117  -0.951 ± 0.353  -1.351 ± 0.337  -0.975 ± 0.159 
+#>  [61] -0.542 ± 0.099  -0.876 ± 0.314  -0.119 ± 0.075  -0.901 ± 0.247 
+#>  [65] -1.995 ± 0.581  -0.511 ± 0.134  -0.279 ± 0.083  -1.057 ± 0.233 
+#>  [69] -0.438 ± 0.083  -0.638 ± 0.235  -0.609 ± 0.207  -0.463 ± 0.169 
+#>  [73] -1.476 ± 0.363  -0.944 ± 0.194  -1.139 ± 0.389  -0.376 ± 0.140 
+#>  [77] -0.875 ± 0.136  -0.489 ± 0.174  -0.764 ± 0.190  -0.545 ± 0.196 
+#>  [81] -0.165 ± 0.100  -0.226 ± 0.138  -0.346 ± 0.081  -0.278 ± 0.091 
+#>  [85] -0.132 ± 0.075  -1.120 ± 0.315  -0.822 ± 0.127  -0.773 ± 0.237 
+#>  [89] -1.270 ± 0.314  -0.263 ± 0.136  -0.388 ± 0.129  -1.484 ± 0.335 
+#>  [93] -0.738 ± 0.218  -0.321 ± 0.088  -0.391 ± 0.110  -1.562 ± 0.280 
+#>  [97] -0.433 ± 0.102  -1.052 ± 0.372  -0.693 ± 0.139  -0.393 ± 0.098 
 #> 
 posterior::as_draws_list(fit)
 #> # A draws_list: 1000 iterations, 4 chains, and 105 variables
 #> 
 #> [chain = 1]
 #> $lp__
-#>  [1] -66 -65 -68 -68 -67 -66 -65 -66 -67 -66
+#>  [1] -66 -65 -66 -65 -67 -65 -65 -64 -67 -69
 #> 
 #> $alpha
-#>  [1] 0.44 0.30 0.65 0.29 0.53 0.53 0.47 0.38 0.32 0.51
+#>  [1]  0.373  0.355  0.389  0.419  0.300  0.178  0.629  0.288  0.016 -0.075
 #> 
 #> $`beta[1]`
-#>  [1] -0.91 -0.57 -0.90 -1.20 -0.59 -0.51 -0.36 -0.19 -1.17 -1.02
+#>  [1] -0.21 -0.42 -1.01 -0.95 -0.16 -0.51 -0.69 -0.67 -0.74 -0.71
 #> 
 #> $`beta[2]`
-#>  [1]  0.088 -0.515 -0.031 -0.653 -0.345 -0.646 -0.154 -0.488 -0.013  0.034
+#>  [1] -0.3841 -0.2843 -0.0223  0.0016 -0.5327 -0.3040 -0.3392 -0.2792  0.0976
+#> [10]  0.1916
 #> 
 #> 
 #> [chain = 2]
 #> $lp__
-#>  [1] -68 -67 -66 -65 -64 -65 -65 -64 -65 -65
+#>  [1] -67 -65 -66 -65 -67 -67 -67 -64 -68 -66
 #> 
 #> $alpha
-#>  [1]  0.414 -0.045  0.397  0.644  0.435  0.266  0.415  0.369  0.229  0.185
+#>  [1] 0.0232 0.1359 0.0021 0.2264 0.6367 0.7152 0.4707 0.3940 0.0634 0.4544
 #> 
 #> $`beta[1]`
-#>  [1] -0.17 -0.97 -0.28 -0.58 -0.74 -0.48 -0.83 -0.72 -0.44 -0.58
+#>  [1] -0.70 -0.81 -0.83 -0.45 -0.96 -0.34 -1.17 -0.57 -0.62 -0.62
 #> 
 #> $`beta[2]`
-#>  [1] -0.192 -0.088 -0.444 -0.229 -0.277 -0.314  0.046 -0.240 -0.080 -0.090
+#>  [1]  0.08 -0.27 -0.13 -0.28 -0.66 -0.57 -0.21 -0.20 -0.47 -0.67
 #> 
 #> # ... with 990 more iterations, and 2 more chains, and 101 more variables
 # }
diff --git a/docs/reference/cmdstan_model.html b/docs/reference/cmdstan_model.html
index 9385aa48c..a94e571ba 100644
--- a/docs/reference/cmdstan_model.html
+++ b/docs/reference/cmdstan_model.html
@@ -358,12 +358,12 @@ 
    Examples
    
 #> 1 lp__        -5.00
 #> 2 theta        0.2 
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -6.79903 
+#> Initial log joint probability = -17.5452 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        3      -6.74802    0.00379849   4.77236e-05      0.9516      0.9516        6    
+#>        5      -6.74802    0.00115356   2.90728e-05           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -394,11 +394,11 @@ 
    Examples
    
 #> Finished in  0.1 seconds.
 fit_laplace$summary()
 #> # A tibble: 3 × 7
-#>   variable      mean median    sd   mad     q5      q95
-#>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
-#> 1 lp__        -7.22  -6.97  0.659 0.305 -8.52  -6.75   
-#> 2 lp_approx__ -0.481 -0.224 0.662 0.308 -1.84  -0.00186
-#> 3 theta        0.272  0.252 0.121 0.122  0.103  0.502  
+#>   variable      mean median    sd   mad      q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
+#> 1 lp__        -7.27  -7.00  0.705 0.338 -8.72   -6.75   
+#> 2 lp_approx__ -0.527 -0.251 0.714 0.340 -2.02   -0.00142
+#> 3 theta        0.270  0.251 0.126 0.125  0.0959  0.509  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -407,8 +407,8 @@ 
    Examples
    
 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 6e-06 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds. 
+#> Gradient evaluation took 5e-06 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -467,6 +467,53 @@ 
    Examples
    
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -11.515672 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.064e-03   1.692e-05    1.000e+00  1.000e+00       126 -6.254e+00 -6.254e+00                   
+#> Path [1] :Best Iter: [3] ELBO (-6.213658) evaluations: (126) 
+#> Path [2] :Initial log joint density = -9.189304 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      3.749e-04   3.327e-06    1.000e+00  1.000e+00       126 -6.289e+00 -6.289e+00                   
+#> Path [2] :Best Iter: [4] ELBO (-6.214534) evaluations: (126) 
+#> Path [3] :Initial log joint density = -13.270061 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.767e-03   4.305e-05    1.000e+00  1.000e+00       126 -6.276e+00 -6.276e+00                   
+#> Path [3] :Best Iter: [3] ELBO (-6.225363) evaluations: (126) 
+#> Path [4] :Initial log joint density = -6.748213 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               2      -6.748e+00      1.004e-02   7.315e-05    1.000e+00  1.000e+00        51 -6.200e+00 -6.200e+00                   
+#> Path [4] :Best Iter: [2] ELBO (-6.200114) evaluations: (51) 
+#> Path [5] :Initial log joint density = -7.368682 
+#> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      5.377e-04   9.870e-07    1.000e+00  1.000e+00       126 -6.276e+00 -6.276e+00                   
+#> Path [5] :Best Iter: [3] ELBO (-6.167297) evaluations: (126) 
+#> Path [6] :Initial log joint density = -9.465368 
+#> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      4.613e-04   4.599e-06    1.000e+00  1.000e+00       126 -6.238e+00 -6.238e+00                   
+#> Path [6] :Best Iter: [3] ELBO (-6.185454) evaluations: (126) 
+#> Path [7] :Initial log joint density = -6.856712 
+#> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      1.028e-04   1.386e-05    9.452e-01  9.452e-01       101 -6.242e+00 -6.242e+00                   
+#> Path [7] :Best Iter: [3] ELBO (-6.159687) evaluations: (101) 
+#> Path [8] :Initial log joint density = -7.085030 
+#> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      3.058e-03   6.268e-05    1.000e+00  1.000e+00       101 -6.221e+00 -6.221e+00                   
+#> Path [8] :Best Iter: [4] ELBO (-6.221460) evaluations: (101) 
+#> Path [9] :Initial log joint density = -7.443992 
+#> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      9.951e-05   3.747e-07    1.000e+00  1.000e+00       126 -6.228e+00 -6.228e+00                   
+#> Path [9] :Best Iter: [2] ELBO (-6.206717) evaluations: (126) 
+#> Path [10] :Initial log joint density = -6.751684 
+#> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               3      -6.748e+00      5.950e-04   1.461e-05    1.000e+00  1.000e+00        76 -6.209e+00 -6.209e+00                   
+#> Path [10] :Best Iter: [3] ELBO (-6.209430) evaluations: (76) 
+#> Total log probability function evaluations:1235 
+#> Finished in  0.1 seconds.
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/docs/reference/cmdstanr-package.html b/docs/reference/cmdstanr-package.html
index 6e29914b9..072311bb7 100644
--- a/docs/reference/cmdstanr-package.html
+++ b/docs/reference/cmdstanr-package.html
@@ -291,7 +291,7 @@ 
    Examples
    
 #> [1] 0 0
 #> 
 #> $ebfmi
-#> [1] 1.0 1.3
+#> [1] 1.017555 1.250490
 #> 
 
 # Get posterior draws
@@ -364,12 +364,12 @@ 
    Examples
    
 #> 1 lp__        -5.00
 #> 2 theta        0.2 
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.20447 
+#> Initial log joint probability = -18.9282 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        4      -6.74802    0.00428356   0.000109727           1           1        7    
+#>        5      -6.74802    0.00032939   4.62604e-06           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -397,14 +397,14 @@ 
    Examples
    
 #> iteration: 1700 
 #> iteration: 1800 
 #> iteration: 1900 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_laplace$summary()
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad      q5      q95
 #>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
-#> 1 lp__        -7.23  -6.96  0.694 0.287 -8.53   -6.75   
-#> 2 lp_approx__ -0.493 -0.211 0.714 0.286 -1.87   -0.00222
-#> 3 theta        0.264  0.248 0.120 0.120  0.0954  0.491  
+#> 1 lp__        -7.23  -6.97  0.683 0.304 -8.57   -6.75   
+#> 2 lp_approx__ -0.496 -0.225 0.743 0.305 -1.91   -0.00217
+#> 3 theta        0.264  0.247 0.120 0.119  0.0967  0.492  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -473,6 +473,53 @@ 
    Examples
    
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -12.637372 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.531e-03   3.268e-05    1.000e+00  1.000e+00       126 -6.214e+00 -6.214e+00                   
+#> Path [1] :Best Iter: [5] ELBO (-6.213940) evaluations: (126) 
+#> Path [2] :Initial log joint density = -11.196746 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      9.877e-04   1.507e-05    1.000e+00  1.000e+00       126 -6.283e+00 -6.283e+00                   
+#> Path [2] :Best Iter: [4] ELBO (-6.223987) evaluations: (126) 
+#> Path [3] :Initial log joint density = -10.337120 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      7.410e-04   9.634e-06    1.000e+00  1.000e+00       126 -6.236e+00 -6.236e+00                   
+#> Path [3] :Best Iter: [2] ELBO (-6.179583) evaluations: (126) 
+#> Path [4] :Initial log joint density = -12.005851 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.253e-03   2.257e-05    1.000e+00  1.000e+00       126 -6.159e+00 -6.159e+00                   
+#> Path [4] :Best Iter: [5] ELBO (-6.159209) evaluations: (126) 
+#> Path [5] :Initial log joint density = -11.377707 
+#> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.032e-03   1.614e-05    1.000e+00  1.000e+00       126 -6.206e+00 -6.206e+00                   
+#> Path [5] :Best Iter: [2] ELBO (-6.202552) evaluations: (126) 
+#> Path [6] :Initial log joint density = -7.049866 
+#> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      2.694e-03   5.075e-05    1.000e+00  1.000e+00       101 -6.203e+00 -6.203e+00                   
+#> Path [6] :Best Iter: [4] ELBO (-6.203218) evaluations: (101) 
+#> Path [7] :Initial log joint density = -9.219130 
+#> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      3.841e-04   3.455e-06    1.000e+00  1.000e+00       126 -6.185e+00 -6.185e+00                   
+#> Path [7] :Best Iter: [5] ELBO (-6.185156) evaluations: (126) 
+#> Path [8] :Initial log joint density = -17.192426 
+#> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.354e-03   3.651e-05    1.000e+00  1.000e+00       126 -6.204e+00 -6.204e+00                   
+#> Path [8] :Best Iter: [4] ELBO (-6.168586) evaluations: (126) 
+#> Path [9] :Initial log joint density = -9.491732 
+#> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      4.697e-04   4.731e-06    1.000e+00  1.000e+00       126 -6.247e+00 -6.247e+00                   
+#> Path [9] :Best Iter: [3] ELBO (-6.220590) evaluations: (126) 
+#> Path [10] :Initial log joint density = -6.750036 
+#> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               3      -6.748e+00      3.322e-04   6.069e-06    1.000e+00  1.000e+00        76 -6.214e+00 -6.214e+00                   
+#> Path [10] :Best Iter: [2] ELBO (-6.188430) evaluations: (76) 
+#> Total log probability function evaluations:1335 
+#> Finished in  0.1 seconds.
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
@@ -488,7 +535,7 @@ 
    Examples
    
 #> 
 #> Both chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.7 seconds.
+#> Total execution time: 0.3 seconds.
 #> 
 fit_mcmc_w_init_fun_2 <- mod$sample(
   data = stan_data,
@@ -517,7 +564,7 @@ 
    Examples
    
 #> 
 #> [[2]]
 #> [[2]]$theta
-#> [1] 0.33
+#> [1] 0.3333333
 #> 
 #> 
 
@@ -539,7 +586,7 @@ 
    Examples
    
 #> 
 #> Both chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.5 seconds.
+#> Total execution time: 0.3 seconds.
 #> 
 fit_optim_w_init_list <- mod$optimize(
   data = stan_data,
diff --git a/docs/reference/cmdstanr_example.html b/docs/reference/cmdstanr_example.html
index babf2ce04..8d377b8f0 100644
--- a/docs/reference/cmdstanr_example.html
+++ b/docs/reference/cmdstanr_example.html
@@ -187,18 +187,18 @@ 
    Examples
    
 fit_logistic_mcmc <- cmdstanr_example("logistic", chains = 2)
 fit_logistic_mcmc$summary()
 #> # A tibble: 105 × 10
-#>    variable      mean  median    sd   mad       q5      q95  rhat ess_bulk
-#>    <chr>        <dbl>   <dbl> <dbl> <dbl>    <dbl>    <dbl> <dbl>    <dbl>
-#>  1 lp__       -66.1   -65.7   1.53  1.26  -69.1    -64.3    1.00     1090.
-#>  2 alpha        0.373   0.371 0.223 0.215   0.0169   0.729  1.00     1944.
-#>  3 beta[1]     -0.678  -0.676 0.260 0.261  -1.11    -0.265  1.00     1936.
-#>  4 beta[2]     -0.276  -0.274 0.234 0.239  -0.674    0.0978 1.00     1841.
-#>  5 beta[3]      0.684   0.681 0.268 0.270   0.270    1.13   1.00     2070.
-#>  6 log_lik[1]  -0.518  -0.509 0.102 0.101  -0.694   -0.366  0.999    1983.
-#>  7 log_lik[2]  -0.398  -0.380 0.149 0.143  -0.665   -0.194  1.00     2108.
-#>  8 log_lik[3]  -0.501  -0.461 0.228 0.210  -0.939   -0.204  1.00     1995.
-#>  9 log_lik[4]  -0.446  -0.428 0.157 0.148  -0.740   -0.226  1.00     1806.
-#> 10 log_lik[5]  -1.18   -1.15  0.285 0.286  -1.67    -0.754  1.00     2253.
+#>    variable      mean  median    sd    mad       q5     q95  rhat ess_bulk
+#>    <chr>        <dbl>   <dbl> <dbl>  <dbl>    <dbl>   <dbl> <dbl>    <dbl>
+#>  1 lp__       -66.0   -65.6   1.45  1.20   -68.7    -64.3   1.00      934.
+#>  2 alpha        0.384   0.386 0.218 0.218    0.0341   0.748 1.00     1959.
+#>  3 beta[1]     -0.667  -0.661 0.247 0.248   -1.08    -0.273 1.00     1840.
+#>  4 beta[2]     -0.275  -0.273 0.231 0.231   -0.662    0.114 1.00     1954.
+#>  5 beta[3]      0.687   0.685 0.271 0.269    0.254    1.13  1.00     2212.
+#>  6 log_lik[1]  -0.515  -0.509 0.101 0.0967  -0.698   -0.360 1.00     2038.
+#>  7 log_lik[2]  -0.402  -0.380 0.150 0.140   -0.673   -0.195 1.00     2565.
+#>  8 log_lik[3]  -0.498  -0.467 0.223 0.212   -0.922   -0.201 1.00     2174.
+#>  9 log_lik[4]  -0.449  -0.430 0.154 0.148   -0.731   -0.232 0.999    1785.
+#> 10 log_lik[5]  -1.19   -1.17  0.283 0.281   -1.69    -0.772 1.00     2119.
 #> # ℹ 95 more rows
 #> # ℹ 1 more variable: ess_tail <dbl>
 
@@ -211,7 +211,7 @@ 
    Examples
    
 #>  2 alpha         0.364
 #>  3 beta[1]      -0.632
 #>  4 beta[2]      -0.259
-#>  5 beta[3]       0.649
+#>  5 beta[3]       0.648
 #>  6 log_lik[1]   -0.515
 #>  7 log_lik[2]   -0.394
 #>  8 log_lik[3]   -0.469
@@ -222,18 +222,18 @@ 
    Examples
    
 fit_logistic_vb <- cmdstanr_example("logistic", method = "variational")
 fit_logistic_vb$summary()
 #> # A tibble: 106 × 7
-#>    variable       mean  median    sd   mad      q5      q95
-#>    <chr>         <dbl>   <dbl> <dbl> <dbl>   <dbl>    <dbl>
-#>  1 lp__        -66.5   -66.1   1.98  1.74  -70.5   -64.4   
-#>  2 lp_approx__  -2.01   -1.63  1.53  1.27   -5.07   -0.341 
-#>  3 alpha         0.305   0.311 0.248 0.234  -0.116   0.716 
-#>  4 beta[1]      -0.623  -0.633 0.230 0.239  -0.987  -0.255 
-#>  5 beta[2]      -0.377  -0.369 0.263 0.258  -0.821   0.0339
-#>  6 beta[3]       0.681   0.671 0.291 0.288   0.215   1.16  
-#>  7 log_lik[1]   -0.565  -0.558 0.118 0.108  -0.766  -0.383 
-#>  8 log_lik[2]   -0.412  -0.388 0.172 0.156  -0.740  -0.186 
-#>  9 log_lik[3]   -0.641  -0.589 0.294 0.280  -1.22   -0.265 
-#> 10 log_lik[4]   -0.389  -0.367 0.145 0.134  -0.662  -0.190 
+#>    variable       mean  median     sd    mad      q5      q95
+#>    <chr>         <dbl>   <dbl>  <dbl>  <dbl>   <dbl>    <dbl>
+#>  1 lp__        -66.5   -66.0   1.82   1.62   -69.9   -64.3   
+#>  2 lp_approx__  -1.96   -1.62  1.41   1.19    -4.62   -0.318 
+#>  3 alpha         0.503   0.492 0.219  0.222    0.144   0.876 
+#>  4 beta[1]      -0.582  -0.568 0.301  0.288   -1.10   -0.0916
+#>  5 beta[2]      -0.278  -0.275 0.185  0.183   -0.592   0.0118
+#>  6 beta[3]       0.688   0.696 0.301  0.307    0.211   1.18  
+#>  7 log_lik[1]   -0.480  -0.475 0.0981 0.0994  -0.648  -0.332 
+#>  8 log_lik[2]   -0.466  -0.443 0.190  0.185   -0.836  -0.197 
+#>  9 log_lik[3]   -0.470  -0.443 0.195  0.188   -0.838  -0.209 
+#> 10 log_lik[4]   -0.495  -0.480 0.152  0.148   -0.776  -0.277 
 #> # ℹ 96 more rows
 
 print_example_program("schools")
@@ -254,23 +254,23 @@ 
    Examples
    
 #>   target += normal_lpdf(y | theta, sigma);
 #> }
 fit_schools_mcmc <- cmdstanr_example("schools")
-#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
+#> Warning: 146 of 4000 (4.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 fit_schools_mcmc$summary()
 #> # A tibble: 11 × 10
 #>    variable   mean median    sd   mad      q5   q95  rhat ess_bulk ess_tail
 #>    <chr>     <dbl>  <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>    <dbl>    <dbl>
-#>  1 lp__     -57.8  -58.2   5.50  5.71 -66.3   -48.3  1.04     151.     248.
-#>  2 mu         6.47   6.50  4.26  4.17  -1.05   13.3  1.01     464.     141.
-#>  3 tau        5.22   4.49  3.59  3.40   0.984  12.2  1.04     115.     122.
-#>  4 theta[1]   9.21   8.56  7.05  6.14  -1.40   21.8  1.01     427.     239.
-#>  5 theta[2]   6.77   6.75  5.62  5.20  -2.79   15.9  1.01     718.     443.
-#>  6 theta[3]   5.35   5.83  6.82  5.87  -6.13   15.5  1.01    1103.    1908.
-#>  7 theta[4]   6.84   6.85  5.86  5.44  -2.42   16.6  1.01     923.    2076.
-#>  8 theta[5]   4.64   4.91  5.62  5.40  -4.88   13.4  1.01     775.    1603.
-#>  9 theta[6]   5.51   5.70  6.05  5.48  -4.39   15.1  1.01     639.    1878.
-#> 10 theta[7]   8.94   8.42  6.14  5.47  -0.805  19.6  1.02     272.     164.
-#> 11 theta[8]   7.08   6.94  6.75  5.70  -3.52   18.0  1.01     969.    1764.
+#>  1 lp__     -58.4  -58.5   5.06  5.42 -66.4   -50.2  1.02     139.     71.0
+#>  2 mu         6.36   6.39  4.24  4.27  -0.684  13.0  1.01     487.    345. 
+#>  3 tau        5.42   4.61  3.53  3.33   1.25   12.3  1.02     136.     67.5
+#>  4 theta[1]   9.11   8.41  7.20  6.29  -1.35   21.8  1.01     774.    601. 
+#>  5 theta[2]   6.65   6.69  5.88  5.59  -2.86   16.2  1.01    1011.   2093. 
+#>  6 theta[3]   5.38   5.61  6.74  6.18  -5.83   15.9  1.01     863.   1953. 
+#>  7 theta[4]   6.59   6.59  6.01  5.73  -2.94   16.6  1.01     946.   2109. 
+#>  8 theta[5]   4.37   4.72  5.66  5.51  -5.63   12.8  1.01     745.   2276. 
+#>  9 theta[6]   5.28   5.49  5.96  5.60  -4.99   14.6  1.01     879.   1751. 
+#> 10 theta[7]   9.06   8.65  6.17  5.72  -0.151  20.1  1.01     678.   1072. 
+#> 11 theta[8]   6.91   6.84  6.86  6.09  -4.10   17.8  1.01     939.   2286. 
 
 print_example_program("schools_ncp")
 #> data {
@@ -295,36 +295,35 @@ 
    Examples
    
 fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")
 fit_schools_ncp_mcmc$summary()
 #> # A tibble: 19 × 10
-#>    variable          mean    median    sd   mad       q5    q95  rhat ess_bulk
-#>    <chr>            <dbl>     <dbl> <dbl> <dbl>    <dbl>  <dbl> <dbl>    <dbl>
-#>  1 lp__         -46.9     -46.6     2.47  2.39  -51.5    -43.5   1.00    1443.
-#>  2 mu             6.50      6.57    4.25  4.13   -0.446   13.4   1.00    2941.
-#>  3 tau            4.78      3.91    3.74  3.46    0.374   12.1   1.00    1743.
-#>  4 theta_raw[1]   0.372     0.381   0.989 0.951  -1.28     1.97  1.00    3537.
-#>  5 theta_raw[2]   0.0565    0.0620  0.915 0.928  -1.45     1.53  1.00    4450.
-#>  6 theta_raw[3]  -0.148    -0.163   0.938 0.932  -1.65     1.40  1.00    3407.
-#>  7 theta_raw[4]   0.00702   0.00718 0.937 0.912  -1.55     1.53  1.00    3544.
-#>  8 theta_raw[5]  -0.264    -0.271   0.896 0.870  -1.70     1.23  1.00    3495.
-#>  9 theta_raw[6]  -0.148    -0.157   0.950 0.938  -1.68     1.43  1.00    4517.
-#> 10 theta_raw[7]   0.351     0.374   0.924 0.876  -1.23     1.82  1.00    3596.
-#> 11 theta_raw[8]   0.0685    0.0540  0.960 0.970  -1.52     1.64  1.00    3657.
-#> 12 theta[1]       8.96      8.30    6.79  5.56   -0.975   21.5   1.00    3374.
-#> 13 theta[2]       6.90      6.72    5.56  5.25   -2.07    16.0   1.00    4663.
-#> 14 theta[3]       5.58      5.91    6.35  5.42   -5.10    15.2   1.00    3392.
-#> 15 theta[4]       6.67      6.72    6.02  5.40   -2.96    16.2   1.00    3971.
-#> 16 theta[5]       4.81      5.19    5.50  5.21   -4.81    12.9   1.00    4119.
-#> 17 theta[6]       5.55      5.86    5.93  5.29   -4.38    14.7   1.00    4010.
-#> 18 theta[7]       8.73      8.24    5.88  5.28   -0.0503  19.3   1.00    3955.
-#> 19 theta[8]       7.01      6.84    6.39  5.49   -3.28    17.9   1.00    3861.
-#> # ℹ 1 more variable: ess_tail <dbl>
+#>    variable     mean   median    sd   mad      q5    q95  rhat ess_bulk ess_tail
+#>    <chr>       <dbl>    <dbl> <dbl> <dbl>   <dbl>  <dbl> <dbl>    <dbl>    <dbl>
+#>  1 lp__     -46.9    -4.66e+1 2.49  2.36  -51.6   -43.6   1.00    1229.    1844.
+#>  2 mu         6.43    6.46e+0 4.08  3.93   -0.238  13.1   1.00    2985.    2197.
+#>  3 tau        4.74    4.03e+0 3.70  3.41    0.334  11.9   1.00    1724.    1496.
+#>  4 theta_r…   0.351   3.53e-1 0.979 0.955  -1.26    1.96  1.00    3404.    2385.
+#>  5 theta_r…   0.0484  6.67e-2 0.924 0.913  -1.49    1.53  1.00    3680.    2435.
+#>  6 theta_r…  -0.131  -1.50e-1 0.962 0.938  -1.71    1.50  1.00    3067.    2557.
+#>  7 theta_r…   0.0221  5.18e-3 0.919 0.905  -1.49    1.59  1.00    3748.    2583.
+#>  8 theta_r…  -0.273  -3.14e-1 0.931 0.892  -1.77    1.34  1.00    4139.    2891.
+#>  9 theta_r…  -0.157  -1.57e-1 0.930 0.929  -1.69    1.36  1.00    3560.    2797.
+#> 10 theta_r…   0.363   4.01e-1 0.924 0.911  -1.15    1.87  1.00    3687.    2941.
+#> 11 theta_r…   0.0558  7.25e-2 0.951 0.924  -1.55    1.65  1.00    4077.    2555.
+#> 12 theta[1]   8.85    8.09e+0 6.85  5.77   -0.638  21.2   1.00    2994.    2531.
+#> 13 theta[2]   6.79    6.75e+0 5.59  5.01   -2.46   15.9   1.00    4593.    3367.
+#> 14 theta[3]   5.40    5.88e+0 6.64  5.63   -5.94   15.5   1.00    3448.    3181.
+#> 15 theta[4]   6.65    6.62e+0 5.69  5.09   -2.25   16.0   1.00    4537.    3155.
+#> 16 theta[5]   4.78    5.06e+0 5.54  5.26   -4.69   13.2   1.00    3996.    3191.
+#> 17 theta[6]   5.44    5.63e+0 5.97  5.29   -4.66   14.8   1.00    3934.    2877.
+#> 18 theta[7]   8.83    8.37e+0 5.98  5.56    0.141  19.4   1.00    4104.    3336.
+#> 19 theta[8]   6.89    6.73e+0 6.34  5.33   -3.03   17.1   1.00    3988.    3025.
 
 # optimization fails for hierarchical model
 cmdstanr_example("schools", "optimize", quiet = FALSE)
-#> Initial log joint probability = -319.937 
+#> Initial log joint probability = -56.2449 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>       99       120.913      0.248985   3.76333e+09           1           1      183    
+#>       99       121.706      0.109668   2.48002e+09           1           1      174    
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>      180       242.851     0.0892321   2.24452e+16       1e-12       0.001      378  LS failed, Hessian reset  
+#>      187       244.785      0.253817       8.00296       1e-12       0.001      400  LS failed, Hessian reset  
 #>   Line search failed to achieve a sufficient decrease, no more progress can be made 
 #> Chain 1 Optimization terminated with error: 
 #> Warning: Fitting finished unexpectedly! Use the $output() method for more information.
diff --git a/docs/reference/draws_to_csv.html b/docs/reference/draws_to_csv.html
index 9f523602e..355224d76 100644
--- a/docs/reference/draws_to_csv.html
+++ b/docs/reference/draws_to_csv.html
@@ -169,10 +169,10 @@ 
    Examples
    
 
 draws_csv_files <- draws_to_csv(draws)
 print(draws_csv_files)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-1-7ea6a2.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-2-7ea6a2.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-3-7ea6a2.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-4-7ea6a2.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-1-1dd4e6.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-2-1dd4e6.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-3-1dd4e6.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-4-1dd4e6.csv"
 
 # draws_csv_files <- draws_to_csv(draws,
 #                                 sampler_diagnostic = sampler_diagnostics,
diff --git a/docs/reference/fit-method-cmdstan_summary.html b/docs/reference/fit-method-cmdstan_summary.html
index 2ab3dc625..47d7f8f7f 100644
--- a/docs/reference/fit-method-cmdstan_summary.html
+++ b/docs/reference/fit-method-cmdstan_summary.html
@@ -149,7 +149,7 @@ 
    Examples
    
     
    # \dontrun{
 fit <- cmdstanr_example("logistic")
 fit$cmdstan_diagnose()
-#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-1-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-2-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-3-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-4-1f791d.csv
+#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-1-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-2-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-3-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-4-2718e9.csv
 #> 
 #> Checking sampler transitions treedepth.
 #> Treedepth satisfactory for all transitions.
@@ -169,123 +169,123 @@ 
    Examples
    
 #> Inference for Stan model: logistic_model
 #> 4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
 #> 
-#> Warmup took (0.023, 0.023, 0.023, 0.023) seconds, 0.092 seconds total
-#> Sampling took (0.076, 0.070, 0.072, 0.073) seconds, 0.29 seconds total
+#> Warmup took (0.022, 0.023, 0.023, 0.023) seconds, 0.091 seconds total
+#> Sampling took (0.071, 0.070, 0.070, 0.070) seconds, 0.28 seconds total
 #> 
 #>                     Mean     MCSE  StdDev     5%       50%       95%  N_Eff  N_Eff/s    R_hat
 #> 
-#> lp__            -6.6e+01  3.0e-02     1.4    -69  -6.6e+01  -6.4e+01   2277     7826      1.0
-#> accept_stat__       0.89  1.8e-03    0.12   0.63      0.94       1.0   4996    17168  1.0e+00
-#> stepsize__          0.80  4.6e-02   0.066   0.73      0.85      0.88    2.0      6.9  2.6e+13
-#> treedepth__          2.3  8.8e-02    0.50    2.0       2.0       3.0     32      109  1.0e+00
-#> n_leapfrog__         4.8  2.8e-01     2.0    3.0       3.0       7.0     53      182  1.0e+00
+#> lp__            -6.6e+01  3.1e-02     1.4    -69  -6.6e+01  -6.4e+01   2120     7545      1.0
+#> accept_stat__       0.91  1.4e-03    0.10   0.69      0.95       1.0   5437    19350  1.0e+00
+#> stepsize__          0.74  2.1e-02   0.030   0.70      0.75      0.78    2.0      7.1  1.1e+13
+#> treedepth__          2.4  8.8e-03    0.53    2.0       2.0       3.0   3601    12815  1.0e+00
+#> n_leapfrog__         5.3  3.3e-02     2.0    3.0       7.0       7.0   3773    13428  1.0e+00
 #> divergent__         0.00      nan    0.00   0.00      0.00      0.00    nan      nan      nan
-#> energy__              68  4.5e-02     2.0     65        68        72   1941     6671  1.0e+00
+#> energy__              68  4.9e-02     2.0     65        68        72   1750     6227  1.0e+00
 #> 
-#> alpha            3.8e-01  3.5e-03    0.22  0.014   3.8e-01   7.4e-01   4054    13931      1.0
-#> beta[1]         -6.7e-01  3.7e-03    0.25   -1.1  -6.7e-01  -2.8e-01   4566    15692      1.0
-#> beta[2]         -2.8e-01  3.9e-03    0.23  -0.66  -2.8e-01   9.5e-02   3521    12100      1.0
-#> beta[3]          6.8e-01  4.0e-03    0.26   0.25   6.8e-01   1.1e+00   4334    14894      1.0
-#> log_lik[1]      -5.2e-01  1.6e-03    0.10  -0.69  -5.1e-01  -3.6e-01   4009    13775      1.0
-#> log_lik[2]      -4.0e-01  2.2e-03    0.15  -0.69  -3.8e-01  -1.9e-01   4671    16052      1.0
-#> log_lik[3]      -5.0e-01  3.7e-03    0.22  -0.91  -4.7e-01  -2.1e-01   3570    12267      1.0
-#> log_lik[4]      -4.5e-01  2.4e-03    0.15  -0.72  -4.3e-01  -2.3e-01   3825    13143      1.0
-#> log_lik[5]      -1.2e+00  4.1e-03    0.28   -1.7  -1.2e+00  -7.6e-01   4632    15916      1.0
-#> log_lik[6]      -5.9e-01  2.8e-03    0.19  -0.93  -5.7e-01  -3.2e-01   4602    15813      1.0
-#> log_lik[7]      -6.4e-01  1.9e-03    0.13  -0.86  -6.3e-01  -4.5e-01   4425    15206      1.0
-#> log_lik[8]      -2.7e-01  2.3e-03    0.14  -0.54  -2.5e-01  -1.0e-01   3551    12204      1.0
-#> log_lik[9]      -6.9e-01  2.6e-03    0.17  -0.99  -6.8e-01  -4.4e-01   4396    15105      1.0
-#> log_lik[10]     -7.5e-01  4.0e-03    0.24   -1.2  -7.2e-01  -4.1e-01   3524    12111      1.0
-#> log_lik[11]     -2.8e-01  1.9e-03    0.12  -0.51  -2.6e-01  -1.2e-01   4067    13978     1.00
-#> log_lik[12]     -5.0e-01  3.5e-03    0.24  -0.94  -4.6e-01  -1.9e-01   4672    16056     1.00
-#> log_lik[13]     -6.6e-01  3.0e-03    0.21   -1.0  -6.4e-01  -3.6e-01   4681    16086      1.0
-#> log_lik[14]     -3.6e-01  2.9e-03    0.17  -0.69  -3.3e-01  -1.4e-01   3667    12602      1.0
-#> log_lik[15]     -2.8e-01  1.6e-03    0.11  -0.47  -2.6e-01  -1.4e-01   4253    14617      1.0
-#> log_lik[16]     -2.7e-01  1.5e-03   0.087  -0.43  -2.6e-01  -1.5e-01   3572    12276      1.0
-#> log_lik[17]     -1.6e+00  4.6e-03    0.29   -2.1  -1.6e+00  -1.2e+00   3940    13539     1.00
-#> log_lik[18]     -4.8e-01  1.7e-03    0.11  -0.68  -4.7e-01  -3.1e-01   4208    14461      1.0
-#> log_lik[19]     -2.3e-01  1.3e-03   0.075  -0.37  -2.2e-01  -1.2e-01   3488    11985     1.00
-#> log_lik[20]     -1.1e-01  1.3e-03   0.078  -0.26  -9.3e-02  -2.8e-02   3400    11685      1.0
-#> log_lik[21]     -2.1e-01  1.4e-03   0.086  -0.37  -2.0e-01  -9.3e-02   3747    12875      1.0
-#> log_lik[22]     -5.7e-01  2.3e-03    0.15  -0.84  -5.5e-01  -3.5e-01   4159    14294      1.0
-#> log_lik[23]     -3.3e-01  2.3e-03    0.14  -0.60  -3.0e-01  -1.4e-01   3876    13319      1.0
-#> log_lik[24]     -1.3e-01  1.1e-03   0.066  -0.26  -1.2e-01  -5.1e-02   3311    11379      1.0
-#> log_lik[25]     -4.5e-01  1.9e-03    0.12  -0.67  -4.4e-01  -2.7e-01   4263    14650      1.0
-#> log_lik[26]     -1.5e+00  4.9e-03    0.34   -2.1  -1.5e+00  -1.0e+00   4863    16712     1.00
-#> log_lik[27]     -3.0e-01  1.9e-03    0.12  -0.53  -2.9e-01  -1.4e-01   3942    13545      1.0
-#> log_lik[28]     -4.4e-01  1.3e-03   0.085  -0.59  -4.4e-01  -3.1e-01   4011    13785      1.0
-#> log_lik[29]     -7.2e-01  3.4e-03    0.23   -1.1  -6.9e-01  -3.9e-01   4401    15125      1.0
-#> log_lik[30]     -6.9e-01  2.9e-03    0.20   -1.0  -6.7e-01  -4.0e-01   4483    15406      1.0
-#> log_lik[31]     -4.9e-01  2.5e-03    0.16  -0.78  -4.7e-01  -2.6e-01   4140    14226      1.0
-#> log_lik[32]     -4.2e-01  1.6e-03    0.11  -0.61  -4.1e-01  -2.7e-01   4397    15110      1.0
-#> log_lik[33]     -4.1e-01  1.9e-03    0.13  -0.64  -4.0e-01  -2.2e-01   4495    15446      1.0
-#> log_lik[34]     -6.2e-02  8.6e-04   0.050  -0.16  -4.8e-02  -1.2e-02   3377    11605      1.0
-#> log_lik[35]     -5.8e-01  2.5e-03    0.18  -0.90  -5.6e-01  -3.2e-01   5084    17471     1.00
-#> log_lik[36]     -3.2e-01  2.0e-03    0.13  -0.56  -3.0e-01  -1.4e-01   4311    14814      1.0
-#> log_lik[37]     -7.1e-01  3.7e-03    0.23   -1.1  -6.8e-01  -3.8e-01   3857    13253      1.0
-#> log_lik[38]     -3.1e-01  2.3e-03    0.15  -0.58  -2.8e-01  -1.2e-01   3910    13435      1.0
-#> log_lik[39]     -1.8e-01  1.8e-03    0.11  -0.39  -1.5e-01  -5.3e-02   3559    12229      1.0
-#> log_lik[40]     -6.8e-01  2.0e-03    0.13  -0.92  -6.7e-01  -4.8e-01   4390    15085      1.0
-#> log_lik[41]     -1.1e+00  4.2e-03    0.26   -1.6  -1.1e+00  -7.5e-01   3790    13024      1.0
-#> log_lik[42]     -9.3e-01  3.1e-03    0.20   -1.3  -9.1e-01  -6.3e-01   4446    15278      1.0
-#> log_lik[43]     -4.0e-01  3.5e-03    0.25  -0.89  -3.5e-01  -1.0e-01   5239    18003     1.00
-#> log_lik[44]     -1.2e+00  3.0e-03    0.19   -1.5  -1.2e+00  -8.9e-01   3904    13415      1.0
-#> log_lik[45]     -3.6e-01  1.8e-03    0.12  -0.57  -3.4e-01  -1.9e-01   4092    14061      1.0
-#> log_lik[46]     -5.8e-01  2.0e-03    0.13  -0.82  -5.7e-01  -3.8e-01   4539    15598      1.0
-#> log_lik[47]     -3.0e-01  2.0e-03    0.13  -0.55  -2.8e-01  -1.3e-01   4158    14288      1.0
-#> log_lik[48]     -3.2e-01  1.3e-03   0.083  -0.47  -3.1e-01  -2.0e-01   4285    14726     1.00
-#> log_lik[49]     -3.2e-01  1.3e-03   0.080  -0.46  -3.1e-01  -2.0e-01   3835    13178      1.0
-#> log_lik[50]     -1.3e+00  4.6e-03    0.33   -1.9  -1.3e+00  -8.0e-01   5110    17559     1.00
-#> log_lik[51]     -2.9e-01  1.4e-03   0.094  -0.45  -2.7e-01  -1.5e-01   4316    14833     1.00
-#> log_lik[52]     -8.3e-01  2.3e-03    0.14   -1.1  -8.3e-01  -6.1e-01   4123    14167      1.0
-#> log_lik[53]     -4.0e-01  2.0e-03    0.13  -0.64  -3.8e-01  -2.2e-01   3978    13669      1.0
-#> log_lik[54]     -3.7e-01  2.2e-03    0.14  -0.63  -3.5e-01  -1.8e-01   4204    14447      1.0
-#> log_lik[55]     -3.9e-01  2.0e-03    0.14  -0.64  -3.7e-01  -1.9e-01   4560    15671      1.0
-#> log_lik[56]     -3.1e-01  2.9e-03    0.19  -0.68  -2.7e-01  -8.9e-02   4172    14335      1.0
-#> log_lik[57]     -6.6e-01  1.9e-03    0.12  -0.87  -6.5e-01  -4.8e-01   4000    13747      1.0
-#> log_lik[58]     -9.6e-01  5.3e-03    0.35   -1.6  -9.1e-01  -4.6e-01   4421    15193      1.0
-#> log_lik[59]     -1.4e+00  5.3e-03    0.35   -2.0  -1.3e+00  -8.4e-01   4356    14970      1.0
-#> log_lik[60]     -9.8e-01  2.5e-03    0.16   -1.3  -9.7e-01  -7.3e-01   4051    13921      1.0
-#> log_lik[61]     -5.4e-01  1.5e-03    0.10  -0.71  -5.3e-01  -3.9e-01   4197    14422      1.0
-#> log_lik[62]     -8.8e-01  4.6e-03    0.31   -1.4  -8.4e-01  -4.4e-01   4561    15674     1.00
-#> log_lik[63]     -1.1e-01  1.3e-03   0.072  -0.26  -9.8e-02  -3.2e-02   3248    11162      1.0
-#> log_lik[64]     -9.1e-01  3.9e-03    0.26   -1.4  -8.9e-01  -5.3e-01   4383    15063      1.0
-#> log_lik[65]     -2.0e+00  9.5e-03    0.59   -3.0  -2.0e+00  -1.1e+00   3828    13154     1.00
-#> log_lik[66]     -5.1e-01  2.0e-03    0.13  -0.75  -5.0e-01  -3.1e-01   4591    15777      1.0
-#> log_lik[67]     -2.7e-01  1.3e-03   0.082  -0.42  -2.7e-01  -1.6e-01   3737    12841     1.00
-#> log_lik[68]     -1.1e+00  3.9e-03    0.24   -1.5  -1.1e+00  -7.0e-01   3952    13582      1.0
-#> log_lik[69]     -4.4e-01  1.3e-03   0.085  -0.59  -4.3e-01  -3.1e-01   3944    13552      1.0
-#> log_lik[70]     -6.3e-01  3.4e-03    0.24   -1.1  -6.0e-01  -3.0e-01   5018    17243      1.0
-#> log_lik[71]     -6.1e-01  2.8e-03    0.21  -0.98  -5.9e-01  -3.2e-01   5227    17963     1.00
-#> log_lik[72]     -4.6e-01  2.6e-03    0.17  -0.78  -4.4e-01  -2.3e-01   4552    15644      1.0
-#> log_lik[73]     -1.5e+00  5.9e-03    0.37   -2.2  -1.5e+00  -9.4e-01   3999    13742      1.0
-#> log_lik[74]     -9.4e-01  3.0e-03    0.19   -1.3  -9.3e-01  -6.5e-01   4092    14063      1.0
-#> log_lik[75]     -1.1e+00  5.6e-03    0.38   -1.8  -1.1e+00  -5.9e-01   4639    15941     1.00
-#> log_lik[76]     -3.7e-01  2.3e-03    0.14  -0.63  -3.5e-01  -1.8e-01   3728    12812      1.0
-#> log_lik[77]     -8.8e-01  2.2e-03    0.14   -1.1  -8.7e-01  -6.6e-01   4154    14274      1.0
-#> log_lik[78]     -4.9e-01  2.4e-03    0.17  -0.80  -4.7e-01  -2.5e-01   4939    16971     1.00
-#> log_lik[79]     -7.7e-01  3.2e-03    0.19   -1.1  -7.5e-01  -4.9e-01   3734    12831      1.0
-#> log_lik[80]     -5.5e-01  2.8e-03    0.20  -0.91  -5.2e-01  -2.7e-01   5052    17361      1.0
-#> log_lik[81]     -1.6e-01  1.6e-03   0.100  -0.35  -1.4e-01  -4.6e-02   4042    13890      1.0
-#> log_lik[82]     -2.2e-01  2.1e-03    0.14  -0.50  -1.9e-01  -6.1e-02   4473    15371      1.0
-#> log_lik[83]     -3.4e-01  1.3e-03   0.081  -0.49  -3.4e-01  -2.2e-01   3946    13559      1.0
-#> log_lik[84]     -2.8e-01  1.4e-03   0.091  -0.44  -2.6e-01  -1.5e-01   4018    13807      1.0
-#> log_lik[85]     -1.3e-01  1.3e-03   0.074  -0.27  -1.1e-01  -4.2e-02   3489    11990      1.0
-#> log_lik[86]     -1.1e+00  4.7e-03    0.32   -1.7  -1.1e+00  -6.4e-01   4742    16295      1.0
-#> log_lik[87]     -8.3e-01  2.0e-03    0.13   -1.0  -8.2e-01  -6.2e-01   4289    14739      1.0
-#> log_lik[88]     -7.8e-01  3.5e-03    0.24   -1.2  -7.5e-01  -4.3e-01   4606    15828     1.00
-#> log_lik[89]     -1.3e+00  4.9e-03    0.32   -1.9  -1.3e+00  -7.9e-01   4346    14936      1.0
-#> log_lik[90]     -2.6e-01  2.1e-03    0.13  -0.51  -2.3e-01  -9.0e-02   4192    14407     1.00
-#> log_lik[91]     -3.9e-01  1.9e-03    0.13  -0.62  -3.7e-01  -2.0e-01   4638    15940      1.0
-#> log_lik[92]     -1.5e+00  5.2e-03    0.34   -2.1  -1.5e+00  -9.9e-01   4259    14636      1.0
-#> log_lik[93]     -7.3e-01  3.6e-03    0.22   -1.1  -7.1e-01  -4.2e-01   3742    12858      1.0
-#> log_lik[94]     -3.2e-01  1.4e-03   0.088  -0.48  -3.1e-01  -1.9e-01   3956    13596      1.0
-#> log_lik[95]     -3.9e-01  1.8e-03    0.11  -0.59  -3.8e-01  -2.3e-01   3846    13217      1.0
-#> log_lik[96]     -1.6e+00  4.5e-03    0.28   -2.1  -1.6e+00  -1.1e+00   3868    13293      1.0
-#> log_lik[97]     -4.3e-01  1.5e-03    0.10  -0.61  -4.2e-01  -2.7e-01   4724    16235      1.0
-#> log_lik[98]     -1.1e+00  5.5e-03    0.37   -1.7  -1.0e+00  -5.2e-01   4560    15668      1.0
-#> log_lik[99]     -7.0e-01  2.2e-03    0.15  -0.95  -6.8e-01  -4.7e-01   4389    15084      1.0
-#> log_lik[100]    -3.9e-01  1.5e-03   0.097  -0.56  -3.8e-01  -2.5e-01   4436    15244     1.00
+#> alpha            3.8e-01  3.5e-03    0.22  0.026   3.8e-01   7.5e-01   3978    14156     1.00
+#> beta[1]         -6.7e-01  4.1e-03    0.26   -1.1  -6.6e-01  -2.6e-01   3779    13447      1.0
+#> beta[2]         -2.7e-01  3.5e-03    0.23  -0.64  -2.7e-01   1.0e-01   4061    14450      1.0
+#> beta[3]          6.7e-01  4.3e-03    0.27   0.24   6.7e-01   1.1e+00   3879    13804     1.00
+#> log_lik[1]      -5.1e-01  1.5e-03   0.097  -0.68  -5.1e-01  -3.7e-01   3951    14060     1.00
+#> log_lik[2]      -4.1e-01  2.3e-03    0.15  -0.69  -3.9e-01  -1.9e-01   4167    14830     1.00
+#> log_lik[3]      -4.9e-01  3.3e-03    0.21  -0.88  -4.6e-01  -2.1e-01   4010    14272     1.00
+#> log_lik[4]      -4.5e-01  2.5e-03    0.15  -0.72  -4.4e-01  -2.4e-01   3751    13350      1.0
+#> log_lik[5]      -1.2e+00  4.4e-03    0.28   -1.7  -1.2e+00  -7.6e-01   4068    14476      1.0
+#> log_lik[6]      -5.9e-01  3.0e-03    0.19  -0.96  -5.7e-01  -3.2e-01   4275    15213      1.0
+#> log_lik[7]      -6.4e-01  2.0e-03    0.13  -0.86  -6.4e-01  -4.5e-01   4183    14885      1.0
+#> log_lik[8]      -2.8e-01  2.3e-03    0.14  -0.55  -2.5e-01  -1.1e-01   3659    13021      1.0
+#> log_lik[9]      -6.9e-01  2.7e-03    0.17   -1.0  -6.7e-01  -4.4e-01   4128    14690      1.0
+#> log_lik[10]     -7.3e-01  3.6e-03    0.22   -1.1  -7.1e-01  -4.1e-01   3894    13857     1.00
+#> log_lik[11]     -2.8e-01  2.1e-03    0.13  -0.54  -2.6e-01  -1.1e-01   3822    13601     1.00
+#> log_lik[12]     -5.0e-01  3.8e-03    0.24  -0.95  -4.6e-01  -1.9e-01   3952    14065     1.00
+#> log_lik[13]     -6.6e-01  3.3e-03    0.21   -1.0  -6.4e-01  -3.5e-01   4181    14878      1.0
+#> log_lik[14]     -3.6e-01  2.6e-03    0.17  -0.67  -3.2e-01  -1.4e-01   4021    14308     1.00
+#> log_lik[15]     -2.8e-01  1.7e-03    0.10  -0.47  -2.6e-01  -1.3e-01   3858    13730     1.00
+#> log_lik[16]     -2.8e-01  1.5e-03   0.091  -0.44  -2.7e-01  -1.5e-01   3706    13189     1.00
+#> log_lik[17]     -1.6e+00  5.0e-03    0.30   -2.1  -1.6e+00  -1.1e+00   3612    12854      1.0
+#> log_lik[18]     -4.8e-01  1.8e-03    0.11  -0.68  -4.7e-01  -3.1e-01   4081    14523      1.0
+#> log_lik[19]     -2.3e-01  1.3e-03   0.079  -0.37  -2.2e-01  -1.2e-01   3751    13347      1.0
+#> log_lik[20]     -1.1e-01  1.3e-03   0.078  -0.26  -9.4e-02  -2.8e-02   3798    13516     1.00
+#> log_lik[21]     -2.1e-01  1.5e-03   0.091  -0.39  -2.0e-01  -9.3e-02   3626    12906     1.00
+#> log_lik[22]     -5.7e-01  2.4e-03    0.15  -0.86  -5.5e-01  -3.5e-01   4061    14452      1.0
+#> log_lik[23]     -3.3e-01  2.3e-03    0.15  -0.61  -3.1e-01  -1.4e-01   3872    13780      1.0
+#> log_lik[24]     -1.4e-01  1.1e-03   0.069  -0.27  -1.2e-01  -5.1e-02   3743    13321     1.00
+#> log_lik[25]     -4.6e-01  1.9e-03    0.12  -0.68  -4.5e-01  -2.8e-01   3975    14145      1.0
+#> log_lik[26]     -1.5e+00  5.8e-03    0.35   -2.1  -1.5e+00  -1.0e+00   3699    13164      1.0
+#> log_lik[27]     -3.1e-01  2.0e-03    0.13  -0.55  -2.9e-01  -1.4e-01   3831    13633     1.00
+#> log_lik[28]     -4.4e-01  1.4e-03   0.086  -0.59  -4.4e-01  -3.1e-01   3970    14130      1.0
+#> log_lik[29]     -7.3e-01  3.4e-03    0.22   -1.1  -7.1e-01  -4.0e-01   4258    15153      1.0
+#> log_lik[30]     -6.9e-01  3.1e-03    0.20   -1.1  -6.8e-01  -4.0e-01   4121    14666     1.00
+#> log_lik[31]     -4.9e-01  2.6e-03    0.17  -0.81  -4.7e-01  -2.5e-01   4046    14398      1.0
+#> log_lik[32]     -4.2e-01  1.7e-03    0.11  -0.62  -4.1e-01  -2.6e-01   3986    14185      1.0
+#> log_lik[33]     -4.1e-01  2.0e-03    0.13  -0.65  -4.0e-01  -2.2e-01   4122    14669      1.0
+#> log_lik[34]     -6.5e-02  9.1e-04   0.054  -0.17  -5.0e-02  -1.3e-02   3544    12612      1.0
+#> log_lik[35]     -5.9e-01  2.8e-03    0.18  -0.92  -5.7e-01  -3.2e-01   4411    15696      1.0
+#> log_lik[36]     -3.3e-01  2.2e-03    0.14  -0.58  -3.0e-01  -1.4e-01   4001    14239      1.0
+#> log_lik[37]     -6.9e-01  3.4e-03    0.22   -1.1  -6.7e-01  -3.7e-01   4045    14394      1.0
+#> log_lik[38]     -3.2e-01  2.4e-03    0.15  -0.60  -2.9e-01  -1.2e-01   3854    13717      1.0
+#> log_lik[39]     -1.8e-01  1.7e-03    0.11  -0.38  -1.5e-01  -5.2e-02   3915    13934     1.00
+#> log_lik[40]     -6.8e-01  2.1e-03    0.13  -0.92  -6.7e-01  -4.8e-01   4145    14752      1.0
+#> log_lik[41]     -1.1e+00  4.2e-03    0.26   -1.6  -1.1e+00  -7.3e-01   3929    13983      1.0
+#> log_lik[42]     -9.3e-01  3.2e-03    0.20   -1.3  -9.1e-01  -6.3e-01   3967    14116      1.0
+#> log_lik[43]     -4.1e-01  4.0e-03    0.26  -0.91  -3.4e-01  -1.0e-01   4191    14913      1.0
+#> log_lik[44]     -1.2e+00  3.1e-03    0.19   -1.5  -1.2e+00  -9.0e-01   3709    13201     1.00
+#> log_lik[45]     -3.6e-01  1.8e-03    0.11  -0.56  -3.4e-01  -1.9e-01   4009    14267     1.00
+#> log_lik[46]     -5.9e-01  2.0e-03    0.13  -0.82  -5.8e-01  -3.8e-01   4248    15119     1.00
+#> log_lik[47]     -3.1e-01  2.2e-03    0.13  -0.55  -2.9e-01  -1.3e-01   3696    13153      1.0
+#> log_lik[48]     -3.2e-01  1.4e-03   0.085  -0.47  -3.2e-01  -2.0e-01   3847    13691      1.0
+#> log_lik[49]     -3.2e-01  1.3e-03   0.082  -0.46  -3.1e-01  -2.0e-01   3716    13223     1.00
+#> log_lik[50]     -1.3e+00  5.4e-03    0.33   -1.9  -1.3e+00  -8.1e-01   3859    13734      1.0
+#> log_lik[51]     -2.9e-01  1.6e-03   0.096  -0.46  -2.8e-01  -1.5e-01   3714    13218      1.0
+#> log_lik[52]     -8.4e-01  2.2e-03    0.14   -1.1  -8.3e-01  -6.2e-01   3985    14182     1.00
+#> log_lik[53]     -4.0e-01  2.1e-03    0.13  -0.66  -3.9e-01  -2.1e-01   3970    14127     1.00
+#> log_lik[54]     -3.7e-01  2.2e-03    0.14  -0.62  -3.5e-01  -1.8e-01   4008    14263     1.00
+#> log_lik[55]     -3.9e-01  2.2e-03    0.14  -0.65  -3.7e-01  -1.9e-01   4107    14614     1.00
+#> log_lik[56]     -3.2e-01  3.2e-03    0.20  -0.70  -2.8e-01  -9.3e-02   3745    13326      1.0
+#> log_lik[57]     -6.5e-01  1.8e-03    0.12  -0.85  -6.5e-01  -4.8e-01   3918    13943      1.0
+#> log_lik[58]     -9.4e-01  5.3e-03    0.35   -1.6  -9.0e-01  -4.5e-01   4317    15364      1.0
+#> log_lik[59]     -1.4e+00  5.7e-03    0.35   -2.0  -1.3e+00  -8.4e-01   3801    13527      1.0
+#> log_lik[60]     -9.8e-01  2.5e-03    0.16   -1.3  -9.7e-01  -7.4e-01   3902    13886     1.00
+#> log_lik[61]     -5.4e-01  1.5e-03   0.097  -0.70  -5.3e-01  -3.9e-01   4078    14513     1.00
+#> log_lik[62]     -8.9e-01  5.0e-03    0.31   -1.5  -8.5e-01  -4.4e-01   3852    13708     1.00
+#> log_lik[63]     -1.2e-01  1.3e-03   0.077  -0.27  -9.9e-02  -3.2e-02   3330    11849     1.00
+#> log_lik[64]     -9.0e-01  3.8e-03    0.25   -1.4  -8.8e-01  -5.3e-01   4445    15817     1.00
+#> log_lik[65]     -2.0e+00  9.8e-03    0.61   -3.1  -2.0e+00  -1.1e+00   3783    13462     1.00
+#> log_lik[66]     -5.1e-01  2.1e-03    0.13  -0.74  -4.9e-01  -3.1e-01   4154    14782      1.0
+#> log_lik[67]     -2.8e-01  1.4e-03   0.085  -0.43  -2.7e-01  -1.6e-01   3827    13618      1.0
+#> log_lik[68]     -1.1e+00  3.8e-03    0.24   -1.5  -1.0e+00  -6.8e-01   4102    14597     1.00
+#> log_lik[69]     -4.3e-01  1.4e-03   0.085  -0.58  -4.3e-01  -3.0e-01   3842    13673      1.0
+#> log_lik[70]     -6.4e-01  3.6e-03    0.24   -1.1  -6.1e-01  -3.1e-01   4371    15555     1.00
+#> log_lik[71]     -6.1e-01  3.2e-03    0.21  -0.99  -5.9e-01  -3.1e-01   4401    15663      1.0
+#> log_lik[72]     -4.6e-01  2.7e-03    0.17  -0.79  -4.5e-01  -2.2e-01   4214    14996      1.0
+#> log_lik[73]     -1.5e+00  6.1e-03    0.37   -2.1  -1.4e+00  -9.2e-01   3671    13065      1.0
+#> log_lik[74]     -9.5e-01  3.0e-03    0.19   -1.3  -9.4e-01  -6.7e-01   3944    14035     1.00
+#> log_lik[75]     -1.2e+00  6.4e-03    0.39   -1.8  -1.1e+00  -5.9e-01   3678    13088     1.00
+#> log_lik[76]     -3.7e-01  2.1e-03    0.13  -0.62  -3.5e-01  -1.8e-01   3976    14151     1.00
+#> log_lik[77]     -8.8e-01  2.2e-03    0.14   -1.1  -8.8e-01  -6.6e-01   4034    14357      1.0
+#> log_lik[78]     -4.8e-01  2.7e-03    0.17  -0.79  -4.6e-01  -2.4e-01   4097    14581     1.00
+#> log_lik[79]     -7.6e-01  2.9e-03    0.18   -1.1  -7.4e-01  -4.8e-01   3889    13840      1.0
+#> log_lik[80]     -5.5e-01  3.0e-03    0.20  -0.91  -5.2e-01  -2.7e-01   4407    15683     1.00
+#> log_lik[81]     -1.7e-01  1.7e-03    0.10  -0.37  -1.4e-01  -4.8e-02   3780    13450      1.0
+#> log_lik[82]     -2.3e-01  2.3e-03    0.14  -0.51  -2.0e-01  -6.3e-02   3943    14031     1.00
+#> log_lik[83]     -3.4e-01  1.3e-03   0.081  -0.48  -3.4e-01  -2.2e-01   3766    13404     1.00
+#> log_lik[84]     -2.7e-01  1.5e-03   0.090  -0.44  -2.6e-01  -1.4e-01   3767    13405     1.00
+#> log_lik[85]     -1.3e-01  1.2e-03   0.075  -0.28  -1.1e-01  -4.1e-02   3780    13453     1.00
+#> log_lik[86]     -1.1e+00  5.0e-03    0.32   -1.7  -1.1e+00  -6.4e-01   4173    14850     1.00
+#> log_lik[87]     -8.3e-01  2.0e-03    0.13   -1.1  -8.2e-01  -6.2e-01   4183    14887     1.00
+#> log_lik[88]     -7.8e-01  3.8e-03    0.24   -1.2  -7.5e-01  -4.2e-01   4157    14794      1.0
+#> log_lik[89]     -1.3e+00  5.1e-03    0.32   -1.9  -1.2e+00  -7.9e-01   3879    13805     1.00
+#> log_lik[90]     -2.6e-01  2.2e-03    0.14  -0.52  -2.4e-01  -9.4e-02   4008    14262      1.0
+#> log_lik[91]     -3.9e-01  2.1e-03    0.13  -0.63  -3.8e-01  -2.1e-01   4144    14746      1.0
+#> log_lik[92]     -1.5e+00  5.6e-03    0.34   -2.1  -1.5e+00  -9.7e-01   3829    13625     1.00
+#> log_lik[93]     -7.5e-01  3.4e-03    0.21   -1.1  -7.2e-01  -4.4e-01   3888    13835     1.00
+#> log_lik[94]     -3.2e-01  1.4e-03   0.087  -0.47  -3.1e-01  -1.9e-01   3803    13533     1.00
+#> log_lik[95]     -3.9e-01  1.9e-03    0.12  -0.60  -3.8e-01  -2.2e-01   3889    13841      1.0
+#> log_lik[96]     -1.6e+00  4.8e-03    0.29   -2.1  -1.6e+00  -1.1e+00   3601    12815     1.00
+#> log_lik[97]     -4.3e-01  1.6e-03    0.10  -0.61  -4.2e-01  -2.7e-01   4067    14472      1.0
+#> log_lik[98]     -1.0e+00  5.6e-03    0.37   -1.7  -1.0e+00  -5.2e-01   4342    15452      1.0
+#> log_lik[99]     -7.0e-01  2.2e-03    0.15  -0.96  -6.9e-01  -4.7e-01   4351    15484     1.00
+#> log_lik[100]    -3.9e-01  1.5e-03   0.097  -0.55  -3.8e-01  -2.4e-01   4019    14301     1.00
 #> 
 #> Samples were drawn using hmc with nuts.
 #> For each parameter, N_Eff is a crude measure of effective sample size,
diff --git a/docs/reference/fit-method-diagnostic_summary.html b/docs/reference/fit-method-diagnostic_summary.html
index 97dce2c24..53c45c1c7 100644
--- a/docs/reference/fit-method-diagnostic_summary.html
+++ b/docs/reference/fit-method-diagnostic_summary.html
@@ -161,34 +161,29 @@ 
    See also
    
     
    Examples
    
     
    # \dontrun{
 fit <- cmdstanr_example("schools")
-#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
-#> Chain 2 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model-11d99572dc9ce.stan', line 14, column 2 to column 41)
-#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
-#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
-#> Chain 2 
-#> Warning: 103 of 4000 (3.0%) transitions ended with a divergence.
+#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 fit$diagnostic_summary()
-#> Warning: 103 of 4000 (3.0%) transitions ended with a divergence.
+#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 #> $num_divergent
-#> [1]  2 40 38 23
+#> [1] 12 18 11 50
 #> 
 #> $num_max_treedepth
 #> [1] 0 0 0 0
 #> 
 #> $ebfmi
-#> [1] 0.2865692 0.3867145 0.2763028 0.3720129
+#> [1] 0.3003970 0.2974182 0.3438194 0.4920025
 #> 
 fit$diagnostic_summary(quiet = TRUE)
 #> $num_divergent
-#> [1]  2 40 38 23
+#> [1] 12 18 11 50
 #> 
 #> $num_max_treedepth
 #> [1] 0 0 0 0
 #> 
 #> $ebfmi
-#> [1] 0.2865692 0.3867145 0.2763028 0.3720129
+#> [1] 0.3003970 0.2974182 0.3438194 0.4920025
 #> 
 # }
 
diff --git a/docs/reference/fit-method-draws-1.png b/docs/reference/fit-method-draws-1.png
index 27dbcc215edcba84e9ddad214c0c299681bbcec6..a6899fc4225783ce9f836494403b096d945a5445 100644
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diff --git a/docs/reference/fit-method-draws.html b/docs/reference/fit-method-draws.html
index 1a2ac8f78..b257cb5d6 100644
--- a/docs/reference/fit-method-draws.html
+++ b/docs/reference/fit-method-draws.html
@@ -203,7 +203,7 @@ 
    Examples
    
 dim(draws)
 #> [1] 1000    4  105
 str(draws)
-#>  'draws_array' num [1:1000, 1:4, 1:105] -65.8 -67 -66.3 -66.2 -66.8 ...
+#>  'draws_array' num [1:1000, 1:4, 1:105] -64.2 -65.3 -65.3 -64.7 -64.8 ...
 #>  - attr(*, "dimnames")=List of 3
 #>   ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
 #>   ..$ chain    : chr [1:4] "1" "2" "3" "4"
@@ -215,12 +215,12 @@ 
    Examples
    
 #> # A draws_matrix: 6 iterations, 1 chains, and 105 variables
 #>     variable
 #> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
-#>    1  -66  0.24   -0.55    0.09    0.39      -0.52      -0.42      -0.32
-#>    2  -67  0.52   -0.85   -0.20    1.28      -0.46      -0.18      -0.26
-#>    3  -66  0.19   -0.80   -0.31    0.20      -0.54      -0.53      -0.62
-#>    4  -66  0.39   -0.57   -0.68    1.03      -0.59      -0.33      -0.81
-#>    5  -67  0.46   -0.49   -0.63    1.16      -0.57      -0.30      -0.72
-#>    6  -65  0.30   -0.44   -0.21    0.28      -0.54      -0.59      -0.56
+#>    1  -64  0.28   -0.73   -0.16    0.73      -0.53      -0.30      -0.39
+#>    2  -65  0.19   -0.42   -0.13    0.37      -0.59      -0.48      -0.53
+#>    3  -65  0.61   -0.93   -0.25    0.83      -0.40      -0.34      -0.30
+#>    4  -65  0.42   -0.81   -0.32    0.94      -0.50      -0.28      -0.41
+#>    5  -65  0.36   -0.41   -0.20    0.39      -0.52      -0.56      -0.52
+#>    6  -67  0.55   -0.24   -0.43    0.24      -0.49      -0.87      -0.75
 #> # ... with 97 more variables
 
 # or can specify 'format' argument to avoid manual conversion
@@ -230,12 +230,12 @@ 
    Examples
    
 #> # A draws_matrix: 6 iterations, 1 chains, and 105 variables
 #>     variable
 #> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
-#>    1  -66  0.24   -0.55    0.09    0.39      -0.52      -0.42      -0.32
-#>    2  -67  0.52   -0.85   -0.20    1.28      -0.46      -0.18      -0.26
-#>    3  -66  0.19   -0.80   -0.31    0.20      -0.54      -0.53      -0.62
-#>    4  -66  0.39   -0.57   -0.68    1.03      -0.59      -0.33      -0.81
-#>    5  -67  0.46   -0.49   -0.63    1.16      -0.57      -0.30      -0.72
-#>    6  -65  0.30   -0.44   -0.21    0.28      -0.54      -0.59      -0.56
+#>    1  -64  0.28   -0.73   -0.16    0.73      -0.53      -0.30      -0.39
+#>    2  -65  0.19   -0.42   -0.13    0.37      -0.59      -0.48      -0.53
+#>    3  -65  0.61   -0.93   -0.25    0.83      -0.40      -0.34      -0.30
+#>    4  -65  0.42   -0.81   -0.32    0.94      -0.50      -0.28      -0.41
+#>    5  -65  0.36   -0.41   -0.20    0.39      -0.52      -0.56      -0.52
+#>    6  -67  0.55   -0.24   -0.43    0.24      -0.49      -0.87      -0.75
 #> # ... with 97 more variables
 
 # can select specific parameters
@@ -244,12 +244,12 @@ 
    Examples
    
 #> , , variable = alpha
 #> 
 #>          chain
-#> iteration    1      2     3    4
-#>         1 0.24  0.310 0.180 0.67
-#>         2 0.52 -0.014 0.351 0.23
-#>         3 0.19  0.366 0.682 0.48
-#>         4 0.39  0.645 0.098 0.49
-#>         5 0.46  0.177 0.677 0.65
+#> iteration    1    2    3    4
+#>         1 0.28 0.27 0.62 0.10
+#>         2 0.19 0.70 0.49 0.71
+#>         3 0.61 0.26 0.89 0.14
+#>         4 0.42 0.39 0.58 0.39
+#>         5 0.36 0.39 0.58 0.48
 #> 
 #> # ... with 995 more iterations
 fit$draws("beta")  # selects entire vector beta
@@ -258,31 +258,31 @@ 
    Examples
    
 #> 
 #>          chain
 #> iteration     1     2     3     4
-#>         1 -0.55 -1.00 -0.41 -0.55
-#>         2 -0.85 -0.45 -0.62 -0.91
-#>         3 -0.80 -0.52 -0.86 -0.50
-#>         4 -0.57 -0.46 -0.50 -0.57
-#>         5 -0.49 -0.97 -1.00 -0.76
+#>         1 -0.73 -0.46 -0.82 -0.55
+#>         2 -0.42 -0.92 -1.18 -1.08
+#>         3 -0.93 -0.52 -0.63 -0.42
+#>         4 -0.81 -0.86 -0.30 -0.72
+#>         5 -0.41 -0.86 -0.30 -0.86
 #> 
 #> , , variable = beta[2]
 #> 
 #>          chain
-#> iteration     1     2      3      4
-#>         1  0.09 -0.32 -0.132 -0.091
-#>         2 -0.20 -0.45 -0.283 -0.479
-#>         3 -0.31 -0.25 -0.335 -0.681
-#>         4 -0.68 -0.25  0.093 -0.717
-#>         5 -0.63 -0.23 -0.227 -0.328
+#> iteration     1     2     3     4
+#>         1 -0.16 -0.31 -0.88 -0.18
+#>         2 -0.13 -0.27 -0.69 -0.21
+#>         3 -0.25 -0.39 -0.38 -0.37
+#>         4 -0.32 -0.43 -0.52 -0.36
+#>         5 -0.20 -0.43 -0.52 -0.42
 #> 
 #> , , variable = beta[3]
 #> 
 #>          chain
 #> iteration    1    2    3    4
-#>         1 0.39 0.47 0.54 0.70
-#>         2 1.28 0.68 0.76 0.83
-#>         3 0.20 0.45 0.66 0.30
-#>         4 1.03 0.30 0.53 0.54
-#>         5 1.16 1.17 0.46 1.29
+#>         1 0.73 0.28 0.87 0.93
+#>         2 0.37 1.26 0.75 0.45
+#>         3 0.83 0.28 0.88 0.83
+#>         4 0.94 0.64 0.71 0.48
+#>         5 0.39 0.64 0.71 0.41
 #> 
 #> # ... with 995 more iterations
 fit$draws(c("alpha", "beta[2]"))
@@ -290,22 +290,22 @@ 
    Examples
    
 #> , , variable = alpha
 #> 
 #>          chain
-#> iteration    1      2     3    4
-#>         1 0.24  0.310 0.180 0.67
-#>         2 0.52 -0.014 0.351 0.23
-#>         3 0.19  0.366 0.682 0.48
-#>         4 0.39  0.645 0.098 0.49
-#>         5 0.46  0.177 0.677 0.65
+#> iteration    1    2    3    4
+#>         1 0.28 0.27 0.62 0.10
+#>         2 0.19 0.70 0.49 0.71
+#>         3 0.61 0.26 0.89 0.14
+#>         4 0.42 0.39 0.58 0.39
+#>         5 0.36 0.39 0.58 0.48
 #> 
 #> , , variable = beta[2]
 #> 
 #>          chain
-#> iteration     1     2      3      4
-#>         1  0.09 -0.32 -0.132 -0.091
-#>         2 -0.20 -0.45 -0.283 -0.479
-#>         3 -0.31 -0.25 -0.335 -0.681
-#>         4 -0.68 -0.25  0.093 -0.717
-#>         5 -0.63 -0.23 -0.227 -0.328
+#> iteration     1     2     3     4
+#>         1 -0.16 -0.31 -0.88 -0.18
+#>         2 -0.13 -0.27 -0.69 -0.21
+#>         3 -0.25 -0.39 -0.38 -0.37
+#>         4 -0.32 -0.43 -0.52 -0.36
+#>         5 -0.20 -0.43 -0.52 -0.42
 #> 
 #> # ... with 995 more iterations
 
@@ -323,21 +323,21 @@ 
    Examples
    
 #> # A draws_matrix: 6 iterations, 1 chains, and 3 variables
 #>     variable
 #> draw beta[1] beta[2] beta[3]
-#>    1   -0.74    0.09    0.55
-#>    2   -0.76    0.39    0.68
-#>    3   -0.43   -0.15    1.06
-#>    4   -1.15   -0.35    0.54
-#>    5   -0.60   -0.26    0.34
-#>    6   -0.30   -0.24    1.03
+#>    1   -0.62  -0.485    0.63
+#>    2   -0.34   0.188    0.77
+#>    3   -0.63  -0.236    0.90
+#>    4   -0.65  -0.392    0.76
+#>    5   -0.57   0.028    0.44
+#>    6   -0.58  -0.299    0.69
 head(fit$draws("beta", format = "df"))
 #> # A draws_df: 6 iterations, 1 chains, and 3 variables
 #>   beta[1] beta[2] beta[3]
-#> 1   -0.74    0.09    0.55
-#> 2   -0.76    0.39    0.68
-#> 3   -0.43   -0.15    1.06
-#> 4   -1.15   -0.35    0.54
-#> 5   -0.60   -0.26    0.34
-#> 6   -0.30   -0.24    1.03
+#> 1   -0.62  -0.485    0.63
+#> 2   -0.34   0.188    0.77
+#> 3   -0.63  -0.236    0.90
+#> 4   -0.65  -0.392    0.76
+#> 5   -0.57   0.028    0.44
+#> 6   -0.58  -0.299    0.69
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
 # }
 
diff --git a/docs/reference/fit-method-grad_log_prob.html b/docs/reference/fit-method-grad_log_prob.html
index 79de7ede2..e6dae257f 100644
--- a/docs/reference/fit-method-grad_log_prob.html
+++ b/docs/reference/fit-method-grad_log_prob.html
@@ -113,19 +113,26 @@ 
    Calculate the log-probability and the gradient w.r.t. each input for a
     
    
 
     
    
-    
    grad_log_prob(unconstrained_variables, jacobian_adjustment = TRUE)
    
+    
    grad_log_prob(
+  unconstrained_variables,
+  jacobian = TRUE,
+  jacobian_adjustment = NULL
+)
    
     
    
 
     
    
     
    Arguments
    
     
    unconstrained_variables
    
-
    (numeric) A vector of unconstrained parameters
-to be passed to grad_log_prob.
    
+
    (numeric) A vector of unconstrained parameters.
    
+
+
+
    jacobian
    
+
    (logical) Whether to include the log-density adjustments from
+un/constraining variables.
    
 
 
 
    jacobian_adjustment
    
-
    (logical) Whether to include the log-density
-adjustments from un/constraining variables.
    
+
    Deprecated. Please use jacobian instead.
    
 
 
    
     
    
diff --git a/docs/reference/fit-method-gradients.html b/docs/reference/fit-method-gradients.html
index 828d0688e..c682dd044 100644
--- a/docs/reference/fit-method-gradients.html
+++ b/docs/reference/fit-method-gradients.html
@@ -131,11 +131,11 @@ 
    Examples
    
 
 # retrieve the gradients
 test$gradients()
-#>   param_idx     value     model finite_diff        error
-#> 1         0  1.376210 -19.10810   -19.10810  5.51201e-09
-#> 2         1 -0.796954  -2.04336    -2.04336  5.51900e-09
-#> 3         2  0.520069 -11.77830   -11.77830 -6.60224e-10
-#> 4         3  0.122833   7.41794     7.41794  4.62926e-08
+#>   param_idx    value     model finite_diff       error
+#> 1         0 1.083910  -6.21416    -6.21416 3.58858e-08
+#> 2         1 1.800640 -33.18970   -33.18970 4.38607e-08
+#> 3         2 0.683175 -19.70780   -19.70780 1.73640e-08
+#> 4         3 1.857330  -9.17992    -9.17992 1.12563e-08
 # }
 
 
    
diff --git a/docs/reference/fit-method-hessian.html b/docs/reference/fit-method-hessian.html
index b08bb4ea4..12bcc5e81 100644
--- a/docs/reference/fit-method-hessian.html
+++ b/docs/reference/fit-method-hessian.html
@@ -113,19 +113,22 @@ 
    Calculate the log-probability , the gradient w.r.t. each input, and the hess
     
    
 
     
    
-    
    hessian(unconstrained_variables, jacobian_adjustment = TRUE)
    
+    
    hessian(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = TRUE)
    
     
    
 
     
    
     
    Arguments
    
     
    unconstrained_variables
    
-
    (numeric) A vector of unconstrained parameters
-to be passed to hessian.
    
+
    (numeric) A vector of unconstrained parameters.
    
+
+
+
    jacobian
    
+
    (logical) Whether to include the log-density adjustments from
+un/constraining variables.
    
 
 
 
    jacobian_adjustment
    
-
    (logical) Whether to include the log-density
-adjustments from un/constraining variables.
    
+
    Deprecated. Please use jacobian instead.
    
 
 
    
     
    
diff --git a/docs/reference/fit-method-init.html b/docs/reference/fit-method-init.html
index e6001cccd..955ed4b1f 100644
--- a/docs/reference/fit-method-init.html
+++ b/docs/reference/fit-method-init.html
@@ -140,11 +140,11 @@ 
    Examples
    
 str(fit$init())
 #> List of 2
 #>  $ :List of 2
-#>   ..$ alpha: num 0.865
-#>   ..$ beta : num [1:3] -0.0306 -0.1349 1.1272
+#>   ..$ alpha: num -0.538
+#>   ..$ beta : num [1:3] -0.0289 1.8267 1.6042
 #>  $ :List of 2
-#>   ..$ alpha: num -1.41
-#>   ..$ beta : num [1:3] 0.143 -1.043 0.9
+#>   ..$ alpha: num -1.4
+#>   ..$ beta : num [1:3] -1.313 -1.04 0.224
 
 # partial inits (only specifying for a subset of parameters)
 init_list <- list(
diff --git a/docs/reference/fit-method-inv_metric.html b/docs/reference/fit-method-inv_metric.html
index feac99707..3705aecd0 100644
--- a/docs/reference/fit-method-inv_metric.html
+++ b/docs/reference/fit-method-inv_metric.html
@@ -140,75 +140,75 @@ 
    Examples
    
 fit$inv_metric()
 #> $`1`
 #>           [,1]      [,2]      [,3]      [,4]
-#> [1,] 0.0461164 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0586249 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0569083 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0695349
+#> [1,] 0.0435508 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0607469 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0503494 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0672705
 #> 
 #> $`2`
 #>           [,1]      [,2]      [,3]      [,4]
-#> [1,] 0.0423827 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0638639 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0463094 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0646605
+#> [1,] 0.0532065 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0574785 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0512835 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0641874
 #> 
 #> $`3`
 #>           [,1]      [,2]      [,3]      [,4]
-#> [1,] 0.0555959 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0595803 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0515264 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0759908
+#> [1,] 0.0502858 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0629888 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0512933 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0661664
 #> 
 #> $`4`
-#>         [,1]     [,2]      [,3]    [,4]
-#> [1,] 0.05104 0.000000 0.0000000 0.00000
-#> [2,] 0.00000 0.051252 0.0000000 0.00000
-#> [3,] 0.00000 0.000000 0.0567318 0.00000
-#> [4,] 0.00000 0.000000 0.0000000 0.06699
+#>           [,1]     [,2]    [,3]      [,4]
+#> [1,] 0.0453724 0.000000 0.00000 0.0000000
+#> [2,] 0.0000000 0.061371 0.00000 0.0000000
+#> [3,] 0.0000000 0.000000 0.04017 0.0000000
+#> [4,] 0.0000000 0.000000 0.00000 0.0682251
 #> 
 fit$inv_metric(matrix=FALSE)
 #> $`1`
-#> [1] 0.0461164 0.0586249 0.0569083 0.0695349
+#> [1] 0.0435508 0.0607469 0.0503494 0.0672705
 #> 
 #> $`2`
-#> [1] 0.0423827 0.0638639 0.0463094 0.0646605
+#> [1] 0.0532065 0.0574785 0.0512835 0.0641874
 #> 
 #> $`3`
-#> [1] 0.0555959 0.0595803 0.0515264 0.0759908
+#> [1] 0.0502858 0.0629888 0.0512933 0.0661664
 #> 
 #> $`4`
-#> [1] 0.0510400 0.0512520 0.0567318 0.0669900
+#> [1] 0.0453724 0.0613710 0.0401700 0.0682251
 #> 
 
 fit <- cmdstanr_example("logistic", metric = "dense_e")
 fit$inv_metric()
 #> $`1`
-#>              [,1]        [,2]         [,3]        [,4]
-#> [1,]  0.048223400 -0.00737038  0.000297676  0.00680596
-#> [2,] -0.007370380  0.05440320 -0.006681250 -0.00162053
-#> [3,]  0.000297676 -0.00668125  0.043576600 -0.00859282
-#> [4,]  0.006805960 -0.00162053 -0.008592820  0.06856220
+#>             [,1]        [,2]        [,3]        [,4]
+#> [1,]  0.04487240 -0.00665557  0.00248036 -0.00336473
+#> [2,] -0.00665557  0.05394230 -0.00841737 -0.00815855
+#> [3,]  0.00248036 -0.00841737  0.04945140 -0.01015960
+#> [4,] -0.00336473 -0.00815855 -0.01015960  0.07289150
 #> 
 #> $`2`
-#>             [,1]        [,2]        [,3]        [,4]
-#> [1,]  0.04746980 -0.00175084 -0.00118626  0.00664285
-#> [2,] -0.00175084  0.06320920 -0.00172180 -0.01259270
-#> [3,] -0.00118626 -0.00172180  0.04810590 -0.00872089
-#> [4,]  0.00664285 -0.01259270 -0.00872089  0.06689500
+#>              [,1]        [,2]        [,3]         [,4]
+#> [1,]  0.044891100 -0.00756532  0.00352846 -0.000462971
+#> [2,] -0.007565320  0.06064960 -0.00809279 -0.004813840
+#> [3,]  0.003528460 -0.00809279  0.05892730 -0.016947100
+#> [4,] -0.000462971 -0.00481384 -0.01694710  0.063878300
 #> 
 #> $`3`
-#>             [,1]        [,2]        [,3]        [,4]
-#> [1,]  0.04482880 -0.00377658 -0.00343049  0.00304829
-#> [2,] -0.00377658  0.04803130 -0.00957526 -0.00566723
-#> [3,] -0.00343049 -0.00957526  0.05099840 -0.00574551
-#> [4,]  0.00304829 -0.00566723 -0.00574551  0.07092750
+#>             [,1]         [,2]        [,3]        [,4]
+#> [1,] 0.047806100  0.000761202  0.00579535  0.00494546
+#> [2,] 0.000761202  0.062074500 -0.00488086 -0.01014510
+#> [3,] 0.005795350 -0.004880860  0.05170230 -0.00609765
+#> [4,] 0.004945460 -0.010145100 -0.00609765  0.07081120
 #> 
 #> $`4`
-#>             [,1]         [,2]         [,3]        [,4]
-#> [1,]  0.04787540 -0.003918010 -0.004273680  0.00253868
-#> [2,] -0.00391801  0.049792000  0.000712128 -0.00453177
-#> [3,] -0.00427368  0.000712128  0.045850800 -0.00639672
-#> [4,]  0.00253868 -0.004531770 -0.006396720  0.07511110
+#>            [,1]         [,2]         [,3]        [,4]
+#> [1,] 0.04387350  0.004639300  0.001929550  0.00526756
+#> [2,] 0.00463930  0.064076200 -0.000902685 -0.00609741
+#> [3,] 0.00192955 -0.000902685  0.045911200 -0.00859578
+#> [4,] 0.00526756 -0.006097410 -0.008595780  0.07087270
 #> 
 # }
 
diff --git a/docs/reference/fit-method-log_prob.html b/docs/reference/fit-method-log_prob.html
index a0a9f638e..561ca052b 100644
--- a/docs/reference/fit-method-log_prob.html
+++ b/docs/reference/fit-method-log_prob.html
@@ -1,5 +1,6 @@
 
-Calculate the log-probability given a provided vector of unconstrained parameters. — fit-method-log_prob • cmdstanrCalculate the log-probability given a provided vector of unconstrained parameters. — fit-method-log_prob • cmdstanr
@@ -104,23 +105,27 @@ 
    Calculate the log-probability given a provided vector of unconstrained param
     
    
 
     
    
-    
    The $log_prob() method provides access to the Stan model's log_prob function
    
+    
    The $log_prob() method provides access to the Stan model's
+log_prob function.
    
     
    
 
     
    
-    
    log_prob(unconstrained_variables, jacobian_adjustment = TRUE)
    
+    
    log_prob(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL)
    
     
    
 
     
    
     
    Arguments
    
     
    unconstrained_variables
    
-
    (numeric) A vector of unconstrained parameters
-to be passed to log_prob.
    
+
    (numeric) A vector of unconstrained parameters.
    
+
+
+
    jacobian
    
+
    (logical) Whether to include the log-density adjustments from
+un/constraining variables.
    
 
 
 
    jacobian_adjustment
    
-
    (logical) Whether to include the log-density
-adjustments from un/constraining variables.
    
+
    Deprecated. Please use jacobian instead.
    
 
 
    
     
    
diff --git a/docs/reference/fit-method-loo.html b/docs/reference/fit-method-loo.html
index 5a1a1743d..54c584664 100644
--- a/docs/reference/fit-method-loo.html
+++ b/docs/reference/fit-method-loo.html
@@ -181,9 +181,9 @@ 
    Examples
    
 #> Computed from 4000 by 100 log-likelihood matrix
 #> 
 #>          Estimate  SE
-#> elpd_loo    -63.7 4.2
+#> elpd_loo    -63.7 4.1
 #> p_loo         3.9 0.5
-#> looic       127.3 8.3
+#> looic       127.4 8.2
 #> ------
 #> Monte Carlo SE of elpd_loo is 0.0.
 #> 
diff --git a/docs/reference/fit-method-lp-1.png b/docs/reference/fit-method-lp-1.png
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zYm=Twf13+Nn<3r~Examples
    
     
    # \dontrun{
 fit_mcmc <- cmdstanr_example("logistic")
 head(fit_mcmc$lp())
-#> [1] -65.4484 -67.4196 -65.8307 -65.3367 -67.3193 -66.8288
+#> [1] -65.6405 -64.4734 -65.4431 -64.9703 -65.8088 -65.6905
 
 fit_mle <- cmdstanr_example("logistic", method = "optimize")
 fit_mle$lp()
diff --git a/docs/reference/fit-method-metadata.html b/docs/reference/fit-method-metadata.html
index f97d066f8..01634904c 100644
--- a/docs/reference/fit-method-metadata.html
+++ b/docs/reference/fit-method-metadata.html
@@ -129,7 +129,7 @@ 
    Examples
    
 #>  $ stan_version_major  : num 2
 #>  $ stan_version_minor  : num 33
 #>  $ stan_version_patch  : num 0
-#>  $ start_datetime      : chr "2023-11-08 17:29:44 UTC"
+#>  $ start_datetime      : chr "2023-12-13 17:04:42 UTC"
 #>  $ method              : chr "sample"
 #>  $ save_warmup         : num 0
 #>  $ thin                : num 1
@@ -146,14 +146,14 @@ 
    Examples
    
 #>  $ num_chains          : num 1
 #>  $ id                  : num [1:4] 1 2 3 4
 #>  $ init                : num [1:4] 2 2 2 2
-#>  $ seed                : num 1.39e+09
+#>  $ seed                : num 1.08e+09
 #>  $ refresh             : num 100
 #>  $ sig_figs            : num -1
-#>  $ profile_file        : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-216cf7.csv"
+#>  $ profile_file        : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-79f552.csv"
 #>  $ stanc_version       : chr "stanc3 v2.33.1"
 #>  $ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
 #>  $ variables           : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#>  $ step_size_adaptation: num [1:4] 0.779 0.737 0.698 0.697
+#>  $ step_size_adaptation: num [1:4] 0.723 0.882 0.802 0.749
 #>  $ model_name          : chr "logistic_model"
 #>  $ adapt_engaged       : num 1
 #>  $ adapt_delta         : num 0.8
@@ -164,9 +164,9 @@ 
    Examples
    
 #>  $ threads_per_chain   : num 1
 #>  $ time                :'data.frame':	4 obs. of  4 variables:
 #>   ..$ chain_id: num [1:4] 1 2 3 4
-#>   ..$ warmup  : num [1:4] 0.022 0.023 0.023 0.023
-#>   ..$ sampling: num [1:4] 0.081 0.074 0.083 0.081
-#>   ..$ total   : num [1:4] 0.103 0.097 0.106 0.104
+#>   ..$ warmup  : num [1:4] 0.022 0.022 0.022 0.022
+#>   ..$ sampling: num [1:4] 0.069 0.065 0.07 0.068
+#>   ..$ total   : num [1:4] 0.091 0.087 0.092 0.09
 #>  $ stan_variable_sizes :List of 4
 #>   ..$ lp__   : num 1
 #>   ..$ alpha  : num 1
@@ -181,7 +181,7 @@ 
    Examples
    
 #>  $ stan_version_major : num 2
 #>  $ stan_version_minor : num 33
 #>  $ stan_version_patch : num 0
-#>  $ start_datetime     : chr "2023-11-08 17:29:46 UTC"
+#>  $ start_datetime     : chr "2023-12-13 17:04:44 UTC"
 #>  $ method             : chr "optimize"
 #>  $ algorithm          : chr "lbfgs"
 #>  $ init_alpha         : num 0.001
@@ -196,10 +196,10 @@ 
    Examples
    
 #>  $ save_iterations    : num 0
 #>  $ id                 : num 1
 #>  $ init               : num 2
-#>  $ seed               : num 1.28e+09
+#>  $ seed               : num 9.12e+08
 #>  $ refresh            : num 100
 #>  $ sig_figs           : num -1
-#>  $ profile_file       : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-65f6b8.csv"
+#>  $ profile_file       : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-7b3d9a.csv"
 #>  $ stanc_version      : chr "stanc3 v2.33.1"
 #>  $ sampler_diagnostics: chr(0) 
 #>  $ variables          : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
@@ -220,7 +220,7 @@ 
    Examples
    
 #>  $ stan_version_major : num 2
 #>  $ stan_version_minor : num 33
 #>  $ stan_version_patch : num 0
-#>  $ start_datetime     : chr "2023-11-08 17:29:46 UTC"
+#>  $ start_datetime     : chr "2023-12-13 17:04:44 UTC"
 #>  $ method             : chr "variational"
 #>  $ algorithm          : chr "meanfield"
 #>  $ iter               : num 50
@@ -232,10 +232,10 @@ 
    Examples
    
 #>  $ output_samples     : num 1000
 #>  $ id                 : num 1
 #>  $ init               : num 2
-#>  $ seed               : num 9.18e+08
+#>  $ seed               : num 3.89e+08
 #>  $ refresh            : num 100
 #>  $ sig_figs           : num -1
-#>  $ profile_file       : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-40a7a2.csv"
+#>  $ profile_file       : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-645608.csv"
 #>  $ stanc_version      : chr "stanc3 v2.33.1"
 #>  $ sampler_diagnostics: chr(0) 
 #>  $ variables          : chr [1:106] "lp__" "lp_approx__" "alpha" "beta[1]" ...
diff --git a/docs/reference/fit-method-mle.html b/docs/reference/fit-method-mle.html
index 53b88a5ec..c9ac58215 100644
--- a/docs/reference/fit-method-mle.html
+++ b/docs/reference/fit-method-mle.html
@@ -147,14 +147,14 @@ 
    Examples
    
     
    # \dontrun{
 fit <- cmdstanr_example("logistic", method = "optimize")
 fit$mle("alpha")
-#>   alpha 
-#> 0.36447 
+#>    alpha 
+#> 0.364472 
 fit$mle("beta")
 #>   beta[1]   beta[2]   beta[3] 
-#> -0.631542 -0.258968  0.648496 
+#> -0.631560 -0.258925  0.648500 
 fit$mle("beta[2]")
 #>   beta[2] 
-#> -0.258968 
+#> -0.258925 
 # }
 
 
    
diff --git a/docs/reference/fit-method-output.html b/docs/reference/fit-method-output.html
index d981dc43a..ba2dc64fb 100644
--- a/docs/reference/fit-method-output.html
+++ b/docs/reference/fit-method-output.html
@@ -166,21 +166,21 @@ 
    Examples
    
 #>     num_chains = 1 (Default)
 #> id = 1 (Default)
 #> data
-#>   file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#>   file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
 #> init = 2 (Default)
 #> random
-#>   seed = 1225363303
+#>   seed = 1467887685
 #> output
-#>   file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-2c7d41.csv
+#>   file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-910e87.csv
 #>   diagnostic_file =  (Default)
 #>   refresh = 100 (Default)
 #>   sig_figs = -1 (Default)
-#>   profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-1ddd0e.csv
+#>   profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-90a553.csv
 #> num_threads = 1 (Default)
 #> 
 #> 
-#> Gradient evaluation took 2.2e-05 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
+#> Gradient evaluation took 1.9e-05 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
 #> Adjust your expectations accordingly!
 #> 
 #> 
@@ -207,9 +207,9 @@ 
    Examples
    
 #> Iteration: 1900 / 2000 [ 95%]  (Sampling)
 #> Iteration: 2000 / 2000 [100%]  (Sampling)
 #> 
-#>  Elapsed Time: 0.025 seconds (Warm-up)
-#>                0.082 seconds (Sampling)
-#>                0.107 seconds (Total)
+#>  Elapsed Time: 0.022 seconds (Warm-up)
+#>                0.072 seconds (Sampling)
+#>                0.094 seconds (Total)
 out <- fit_mcmc$output()
 str(out)
 #> List of 4
@@ -237,21 +237,21 @@ 
    Examples
    
 #>     save_iterations = 0 (Default)
 #> id = 1 (Default)
 #> data
-#>   file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#>   file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
 #> init = 2 (Default)
 #> random
-#>   seed = 152179067
+#>   seed = 44204223
 #> output
-#>   file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-0168cf.csv
+#>   file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-75dd88.csv
 #>   diagnostic_file =  (Default)
 #>   refresh = 100 (Default)
 #>   sig_figs = -1 (Default)
-#>   profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-216c50.csv
+#>   profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-5c2d53.csv
 #> num_threads = 1 (Default)
 #> 
-#> Initial log joint probability = -95.9632
+#> Initial log joint probability = -80.1217
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
-#>        7      -63.9218   0.000242271   7.03951e-05           1           1        9   
+#>        7      -63.9218    0.00150318    0.00093398           1           1        9   
 #> Optimization terminated normally: 
 #>   Convergence detected: relative gradient magnitude is below tolerance
 
@@ -274,16 +274,16 @@ 
    Examples
    
 #>     output_samples = 1000 (Default)
 #> id = 1 (Default)
 #> data
-#>   file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#>   file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
 #> init = 2 (Default)
 #> random
-#>   seed = 676659604
+#>   seed = 244304498
 #> output
-#>   file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-6087e5.csv
+#>   file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-3b4b11.csv
 #>   diagnostic_file =  (Default)
 #>   refresh = 100 (Default)
 #>   sig_figs = -1 (Default)
-#>   profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-8ae7e6.csv
+#>   profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-85d291.csv
 #> num_threads = 1 (Default)
 #> 
 #> ------------------------------------------------------------
@@ -294,8 +294,8 @@ 
    Examples
    
 #> 
 #> 
 #> 
-#> Gradient evaluation took 1.8e-05 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
+#> Gradient evaluation took 1.9e-05 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
 #> Adjust your expectations accordingly!
 #> 
 #> 
@@ -309,9 +309,9 @@ 
    Examples
    
 #> 
 #> Begin stochastic gradient ascent.
 #>   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
-#>    100          -66.040             1.000            1.000
-#>    200          -66.445             0.503            1.000
-#>    300          -66.355             0.336            0.006   MEDIAN ELBO CONVERGED
+#>    100          -66.299             1.000            1.000
+#>    200          -66.336             0.500            1.000
+#>    300          -66.628             0.335            0.004   MEDIAN ELBO CONVERGED
 #> 
 #> Drawing a sample of size 1000 from the approximate posterior... 
 #> COMPLETED.
diff --git a/docs/reference/fit-method-profiles.html b/docs/reference/fit-method-profiles.html
index 6d97a13a8..7c4e4b8c4 100644
--- a/docs/reference/fit-method-profiles.html
+++ b/docs/reference/fit-method-profiles.html
@@ -176,32 +176,32 @@ 
    Examples
    
 fit$profiles()
 #> [[1]]
 #>         name   thread_id  total_time forward_time reverse_time chain_stack
-#> 1         gq 0x1061d4e00 0.000364609  0.000364609  0.000000000           0
-#> 2 likelihood 0x1061d4e00 0.001171810  0.000840557  0.000331257        7169
+#> 1         gq 0x10efcde00 0.000375429  0.000375429  0.000000000           0
+#> 2 likelihood 0x10efcde00 0.001165040  0.000834157  0.000330887        7169
 #>   no_chain_stack autodiff_calls no_autodiff_calls
 #> 1              0              0              1000
 #> 2           7169           7169                 1
 #> 
 #> [[2]]
 #>         name   thread_id  total_time forward_time reverse_time chain_stack
-#> 1         gq 0x10e36de00 0.000346564  0.000346564  0.000000000           0
-#> 2 likelihood 0x10e36de00 0.001186840  0.000842553  0.000344283        7155
+#> 1         gq 0x10d0b0e00 0.000407951  0.000407951  0.000000000           0
+#> 2 likelihood 0x10d0b0e00 0.001283690  0.000915781  0.000367905        7155
 #>   no_chain_stack autodiff_calls no_autodiff_calls
 #> 1              0              0              1000
 #> 2           7155           7155                 1
 #> 
 #> [[3]]
 #>         name   thread_id  total_time forward_time reverse_time chain_stack
-#> 1         gq 0x111ab6e00 0.000351792  0.000351792  0.000000000           0
-#> 2 likelihood 0x111ab6e00 0.001143140  0.000829663  0.000313482        6879
+#> 1         gq 0x106bb1e00 0.000399315  0.000399315  0.000000000           0
+#> 2 likelihood 0x106bb1e00 0.001242900  0.000887379  0.000355522        6879
 #>   no_chain_stack autodiff_calls no_autodiff_calls
 #> 1              0              0              1000
 #> 2           6879           6879                 1
 #> 
 #> [[4]]
 #>         name   thread_id  total_time forward_time reverse_time chain_stack
-#> 1         gq 0x10c867e00 0.000408638  0.000408638  0.000000000           0
-#> 2 likelihood 0x10c867e00 0.001279700  0.000924260  0.000355437        6892
+#> 1         gq 0x111b21e00 0.000516567  0.000516567  0.000000000           0
+#> 2 likelihood 0x111b21e00 0.001479350  0.001061390  0.000417962        6892
 #>   no_chain_stack autodiff_calls no_autodiff_calls
 #> 1              0              0              1000
 #> 2           6892           6892                 1
diff --git a/docs/reference/fit-method-return_codes.html b/docs/reference/fit-method-return_codes.html
index 734fbae71..83a654e06 100644
--- a/docs/reference/fit-method-return_codes.html
+++ b/docs/reference/fit-method-return_codes.html
@@ -130,9 +130,7 @@ 
    Examples
    
     
    # \dontrun{
 # example with return codes all zero
 fit_mcmc <- cmdstanr_example("schools", method = "sample")
-#> Warning: 238 of 4000 (6.0%) transitions ended with a divergence.
-#> See https://mc-stan.org/misc/warnings for details.
-#> Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+#> Warning: 72 of 4000 (2.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 fit_mcmc$return_codes() # should be all zero
 #> [1] 0 0 0 0
diff --git a/docs/reference/fit-method-sampler_diagnostics.html b/docs/reference/fit-method-sampler_diagnostics.html
index e83c1bf52..69c929282 100644
--- a/docs/reference/fit-method-sampler_diagnostics.html
+++ b/docs/reference/fit-method-sampler_diagnostics.html
@@ -152,7 +152,7 @@ 
    Examples
    
 fit <- cmdstanr_example("logistic")
 sampler_diagnostics <- fit$sampler_diagnostics()
 str(sampler_diagnostics)
-#>  'draws_array' num [1:1000, 1:4, 1:6] 2 2 2 2 2 3 2 2 2 3 ...
+#>  'draws_array' num [1:1000, 1:4, 1:6] 2 2 2 2 3 2 2 3 2 2 ...
 #>  - attr(*, "dimnames")=List of 3
 #>   ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
 #>   ..$ chain    : chr [1:4] "1" "2" "3" "4"
@@ -162,16 +162,16 @@ 
    Examples
    
 as_draws_df(sampler_diagnostics)
 #> # A draws_df: 1000 iterations, 4 chains, and 6 variables
 #>    treedepth__ divergent__ energy__ accept_stat__ stepsize__ n_leapfrog__
-#> 1            2           0       70          0.76       0.73            3
-#> 2            2           0       69          1.00       0.73            3
-#> 3            2           0       69          1.00       0.73            3
-#> 4            2           0       66          1.00       0.73            3
-#> 5            2           0       66          0.97       0.73            3
-#> 6            3           0       67          0.88       0.73            7
-#> 7            2           0       72          0.74       0.73            3
-#> 8            2           0       73          1.00       0.73            3
-#> 9            2           0       69          1.00       0.73            3
-#> 10           3           0       66          0.95       0.73            7
+#> 1            2           0       66          0.87       0.85            3
+#> 2            2           0       66          0.91       0.85            3
+#> 3            2           0       66          0.99       0.85            3
+#> 4            2           0       65          1.00       0.85            3
+#> 5            3           0       65          0.94       0.85            7
+#> 6            2           0       68          0.83       0.85            7
+#> 7            2           0       66          0.93       0.85            7
+#> 8            3           0       67          0.94       0.85            7
+#> 9            2           0       66          1.00       0.85            3
+#> 10           2           0       68          0.65       0.85            3
 #> # ... with 3990 more draws
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
 
@@ -179,16 +179,16 @@ 
    Examples
    
 fit$sampler_diagnostics(format = "df")
 #> # A draws_df: 1000 iterations, 4 chains, and 6 variables
 #>    treedepth__ divergent__ energy__ accept_stat__ stepsize__ n_leapfrog__
-#> 1            2           0       70          0.76       0.73            3
-#> 2            2           0       69          1.00       0.73            3
-#> 3            2           0       69          1.00       0.73            3
-#> 4            2           0       66          1.00       0.73            3
-#> 5            2           0       66          0.97       0.73            3
-#> 6            3           0       67          0.88       0.73            7
-#> 7            2           0       72          0.74       0.73            3
-#> 8            2           0       73          1.00       0.73            3
-#> 9            2           0       69          1.00       0.73            3
-#> 10           3           0       66          0.95       0.73            7
+#> 1            2           0       66          0.87       0.85            3
+#> 2            2           0       66          0.91       0.85            3
+#> 3            2           0       66          0.99       0.85            3
+#> 4            2           0       65          1.00       0.85            3
+#> 5            3           0       65          0.94       0.85            7
+#> 6            2           0       68          0.83       0.85            7
+#> 7            2           0       66          0.93       0.85            7
+#> 8            3           0       67          0.94       0.85            7
+#> 9            2           0       66          1.00       0.85            3
+#> 10           2           0       68          0.65       0.85            3
 #> # ... with 3990 more draws
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
 # }
diff --git a/docs/reference/fit-method-save_object.html b/docs/reference/fit-method-save_object.html
index 90155c944..8464814fb 100644
--- a/docs/reference/fit-method-save_object.html
+++ b/docs/reference/fit-method-save_object.html
@@ -148,16 +148,16 @@ 
    Examples
    
 #> # A tibble: 105 × 10
 #>    variable      mean  median     sd    mad       q5      q95  rhat ess_bulk
 #>    <chr>        <dbl>   <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl>    <dbl>
-#>  1 lp__       -66.0   -65.6   1.43   1.24   -68.6    -64.3     1.00    2220.
-#>  2 alpha        0.383   0.380 0.214  0.213    0.0497   0.741   1.00    4650.
-#>  3 beta[1]     -0.669  -0.668 0.251  0.258   -1.08    -0.266   1.00    4431.
-#>  4 beta[2]     -0.274  -0.269 0.226  0.224   -0.644    0.0995  1.00    3964.
-#>  5 beta[3]      0.675   0.670 0.269  0.269    0.243    1.13    1.00    3857.
-#>  6 log_lik[1]  -0.513  -0.506 0.0969 0.0968  -0.678   -0.368   1.00    4700.
-#>  7 log_lik[2]  -0.407  -0.386 0.151  0.144   -0.689   -0.194   1.00    4212.
-#>  8 log_lik[3]  -0.496  -0.460 0.215  0.203   -0.888   -0.211   1.00    4110.
-#>  9 log_lik[4]  -0.452  -0.436 0.152  0.146   -0.732   -0.233   1.00    4273.
-#> 10 log_lik[5]  -1.18   -1.16  0.274  0.268   -1.65    -0.760   1.00    4342.
+#>  1 lp__       -65.9   -65.6   1.40   1.17   -68.7    -64.3     1.00    1893.
+#>  2 alpha        0.380   0.380 0.218  0.216    0.0231   0.740   1.00    4801.
+#>  3 beta[1]     -0.668  -0.659 0.248  0.247   -1.09    -0.273   1.00    4573.
+#>  4 beta[2]     -0.271  -0.267 0.224  0.229   -0.634    0.0845  1.00    4645.
+#>  5 beta[3]      0.678   0.666 0.266  0.259    0.248    1.12    1.00    4505.
+#>  6 log_lik[1]  -0.515  -0.508 0.0990 0.0965  -0.689   -0.362   1.00    4844.
+#>  7 log_lik[2]  -0.403  -0.384 0.147  0.139   -0.669   -0.199   1.00    4585.
+#>  8 log_lik[3]  -0.495  -0.459 0.218  0.202   -0.898   -0.206   1.00    4890.
+#>  9 log_lik[4]  -0.452  -0.433 0.153  0.150   -0.726   -0.239   1.00    4491.
+#> 10 log_lik[5]  -1.18   -1.16  0.278  0.275   -1.67    -0.765   1.00    5130.
 #> # ℹ 95 more rows
 #> # ℹ 1 more variable: ess_tail <dbl>
 # }
diff --git a/docs/reference/fit-method-save_output_files.html b/docs/reference/fit-method-save_output_files.html
index f096f70ec..8fb2b772d 100644
--- a/docs/reference/fit-method-save_output_files.html
+++ b/docs/reference/fit-method-save_output_files.html
@@ -210,33 +210,33 @@ 
    Examples
    
     
    # \dontrun{
 fit <- cmdstanr_example()
 fit$output_files()
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-1-98eced.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-2-98eced.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-3-98eced.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-4-98eced.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-1-212881.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-2-212881.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-3-212881.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-4-212881.csv"
 fit$data_file()
-#> [1] "/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json"
+#> [1] "/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json"
 
 # just using tempdir for the example
 my_dir <- tempdir()
 fit$save_output_files(dir = my_dir, basename = "banana")
 #> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-1-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-2-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-3-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-4-846feb.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-1-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-2-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-3-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-4-932dd0.csv
 fit$save_output_files(dir = my_dir, basename = "tomato", timestamp = FALSE)
 #> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-1-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-2-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-3-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-4-270839.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-1-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-2-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-3-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-4-1f5444.csv
 fit$save_output_files(dir = my_dir, basename = "lettuce", timestamp = FALSE, random = FALSE)
 #> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-1.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-2.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-3.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-4.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-1.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-2.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-3.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-4.csv
 # }
 
 
    
diff --git a/docs/reference/fit-method-summary.html b/docs/reference/fit-method-summary.html
index 6f8199faa..14110b92b 100644
--- a/docs/reference/fit-method-summary.html
+++ b/docs/reference/fit-method-summary.html
@@ -165,36 +165,36 @@ 
    Examples
    
 #> # A tibble: 105 × 10
 #>    variable      mean  median     sd    mad       q5      q95  rhat ess_bulk
 #>    <chr>        <dbl>   <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl>    <dbl>
-#>  1 lp__       -65.9   -65.6   1.43   1.23   -68.7    -64.3     1.00    2038.
-#>  2 alpha        0.376   0.369 0.215  0.214    0.0214   0.727   1.00    4221.
-#>  3 beta[1]     -0.665  -0.659 0.247  0.241   -1.08    -0.263   1.00    4093.
-#>  4 beta[2]     -0.275  -0.271 0.224  0.221   -0.652    0.0850  1.00    4052.
-#>  5 beta[3]      0.676   0.669 0.268  0.268    0.244    1.12    1.00    4213.
-#>  6 log_lik[1]  -0.517  -0.511 0.0979 0.0959  -0.683   -0.369   1.00    4266.
-#>  7 log_lik[2]  -0.404  -0.383 0.147  0.140   -0.669   -0.200   1.00    4613.
-#>  8 log_lik[3]  -0.501  -0.466 0.218  0.200   -0.913   -0.209   1.00    4197.
-#>  9 log_lik[4]  -0.450  -0.430 0.153  0.145   -0.724   -0.229   1.00    4069.
-#> 10 log_lik[5]  -1.18   -1.15  0.278  0.270   -1.67    -0.758   1.00    4386.
+#>  1 lp__       -66.0   -65.7   1.48   1.23   -68.9    -64.3     1.00    1956.
+#>  2 alpha        0.382   0.381 0.221  0.222    0.0263   0.744   1.00    3982.
+#>  3 beta[1]     -0.665  -0.661 0.252  0.253   -1.09    -0.264   1.00    4131.
+#>  4 beta[2]     -0.274  -0.280 0.226  0.226   -0.640    0.0976  1.00    3816.
+#>  5 beta[3]      0.675   0.673 0.268  0.265    0.238    1.12    1.00    3963.
+#>  6 log_lik[1]  -0.514  -0.508 0.0987 0.0980  -0.686   -0.364   1.00    3980.
+#>  7 log_lik[2]  -0.408  -0.385 0.153  0.142   -0.690   -0.197   1.00    4263.
+#>  8 log_lik[3]  -0.498  -0.467 0.217  0.204   -0.889   -0.206   1.00    3904.
+#>  9 log_lik[4]  -0.453  -0.436 0.152  0.148   -0.732   -0.236   1.00    3847.
+#> 10 log_lik[5]  -1.18   -1.16  0.285  0.281   -1.68    -0.749   1.00    4407.
 #> # ℹ 95 more rows
 #> # ℹ 1 more variable: ess_tail <dbl>
 fit$print()
 #>    variable   mean median   sd  mad     q5    q95 rhat ess_bulk ess_tail
-#>  lp__       -65.92 -65.59 1.43 1.23 -68.71 -64.30 1.00     2038     2638
-#>  alpha        0.38   0.37 0.21 0.21   0.02   0.73 1.00     4221     3302
-#>  beta[1]     -0.67  -0.66 0.25 0.24  -1.08  -0.26 1.00     4092     2987
-#>  beta[2]     -0.28  -0.27 0.22 0.22  -0.65   0.09 1.00     4052     3349
-#>  beta[3]      0.68   0.67 0.27 0.27   0.24   1.12 1.00     4212     2892
-#>  log_lik[1]  -0.52  -0.51 0.10 0.10  -0.68  -0.37 1.00     4265     3448
-#>  log_lik[2]  -0.40  -0.38 0.15 0.14  -0.67  -0.20 1.00     4612     3246
-#>  log_lik[3]  -0.50  -0.47 0.22 0.20  -0.91  -0.21 1.00     4197     3040
-#>  log_lik[4]  -0.45  -0.43 0.15 0.14  -0.72  -0.23 1.00     4069     2989
-#>  log_lik[5]  -1.18  -1.15 0.28 0.27  -1.67  -0.76 1.00     4386     2650
+#>  lp__       -66.00 -65.68 1.48 1.23 -68.89 -64.30 1.00     1956     2665
+#>  alpha        0.38   0.38 0.22 0.22   0.03   0.74 1.00     3981     3124
+#>  beta[1]     -0.66  -0.66 0.25 0.25  -1.09  -0.26 1.00     4131     3206
+#>  beta[2]     -0.27  -0.28 0.23 0.23  -0.64   0.10 1.00     3815     2736
+#>  beta[3]      0.67   0.67 0.27 0.26   0.24   1.12 1.00     3963     3257
+#>  log_lik[1]  -0.51  -0.51 0.10 0.10  -0.69  -0.36 1.00     3979     2841
+#>  log_lik[2]  -0.41  -0.39 0.15 0.14  -0.69  -0.20 1.00     4263     3051
+#>  log_lik[3]  -0.50  -0.47 0.22 0.20  -0.89  -0.21 1.00     3904     3141
+#>  log_lik[4]  -0.45  -0.44 0.15 0.15  -0.73  -0.24 1.00     3847     2904
+#>  log_lik[5]  -1.18  -1.16 0.28 0.28  -1.68  -0.75 1.00     4406     3095
 #> 
 #>  # showing 10 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option)
 fit$print(max_rows = 2) # same as print(fit, max_rows = 2)
 #>  variable   mean median   sd  mad     q5    q95 rhat ess_bulk ess_tail
-#>     lp__  -65.92 -65.59 1.43 1.23 -68.71 -64.30 1.00     2038     2638
-#>     alpha   0.38   0.37 0.21 0.21   0.02   0.73 1.00     4221     3302
+#>     lp__  -66.00 -65.68 1.48 1.23 -68.89 -64.30 1.00     1956     2665
+#>     alpha   0.38   0.38 0.22 0.22   0.03   0.74 1.00     3981     3124
 #> 
 #>  # showing 2 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option)
 
@@ -203,29 +203,29 @@ 
    Examples
    
 #> # A tibble: 3 × 10
 #>   variable   mean median    sd   mad     q5     q95  rhat ess_bulk ess_tail
 #>   <chr>     <dbl>  <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl>    <dbl>    <dbl>
-#> 1 beta[1]  -0.665 -0.659 0.247 0.241 -1.08  -0.263   1.00    4093.    2988.
-#> 2 beta[2]  -0.275 -0.271 0.224 0.221 -0.652  0.0850  1.00    4052.    3350.
-#> 3 beta[3]   0.676  0.669 0.268 0.268  0.244  1.12    1.00    4213.    2892.
+#> 1 beta[1]  -0.665 -0.661 0.252 0.253 -1.09  -0.264   1.00    4131.    3207.
+#> 2 beta[2]  -0.274 -0.280 0.226 0.226 -0.640  0.0976  1.00    3816.    2736.
+#> 3 beta[3]   0.675  0.673 0.268 0.265  0.238  1.12    1.00    3963.    3257.
 fit$print(c("alpha", "beta[2]"))
 #>  variable  mean median   sd  mad    q5  q95 rhat ess_bulk ess_tail
-#>   alpha    0.38   0.37 0.21 0.21  0.02 0.73 1.00     4221     3302
-#>   beta[2] -0.28  -0.27 0.22 0.22 -0.65 0.09 1.00     4052     3349
+#>   alpha    0.38   0.38 0.22 0.22  0.03 0.74 1.00     3981     3124
+#>   beta[2] -0.27  -0.28 0.23 0.23 -0.64 0.10 1.00     3815     2736
 
 # include all variables but only certain summaries
 fit$summary(NULL, c("mean", "sd"))
 #> # A tibble: 105 × 3
 #>    variable      mean     sd
 #>    <chr>        <dbl>  <dbl>
-#>  1 lp__       -65.9   1.43  
-#>  2 alpha        0.376 0.215 
-#>  3 beta[1]     -0.665 0.247 
-#>  4 beta[2]     -0.275 0.224 
-#>  5 beta[3]      0.676 0.268 
-#>  6 log_lik[1]  -0.517 0.0979
-#>  7 log_lik[2]  -0.404 0.147 
-#>  8 log_lik[3]  -0.501 0.218 
-#>  9 log_lik[4]  -0.450 0.153 
-#> 10 log_lik[5]  -1.18  0.278 
+#>  1 lp__       -66.0   1.48  
+#>  2 alpha        0.382 0.221 
+#>  3 beta[1]     -0.665 0.252 
+#>  4 beta[2]     -0.274 0.226 
+#>  5 beta[3]      0.675 0.268 
+#>  6 log_lik[1]  -0.514 0.0987
+#>  7 log_lik[2]  -0.408 0.153 
+#>  8 log_lik[3]  -0.498 0.217 
+#>  9 log_lik[4]  -0.453 0.152 
+#> 10 log_lik[5]  -1.18  0.285 
 #> # ℹ 95 more rows
 
 # can use functions created from formulas
@@ -234,9 +234,9 @@ 
    Examples
    
 #> # A tibble: 3 × 2
 #>   variable prob_gt_0
 #>   <chr>        <dbl>
-#> 1 beta[1]    0.00225
-#> 2 beta[2]    0.107  
-#> 3 beta[3]    0.995  
+#> 1 beta[1]    0.00325
+#> 2 beta[2]    0.114  
+#> 3 beta[3]    0.997  
 
 # can combine user-specified functions with
 # the default summary functions
@@ -248,10 +248,10 @@ 
    Examples
    
 #> # A tibble: 4 × 10
 #>   variable   mean median    sd   mad    q2.5  q97.5  rhat ess_bulk ess_tail
 #>   <chr>     <dbl>  <dbl> <dbl> <dbl>   <dbl>  <dbl> <dbl>    <dbl>    <dbl>
-#> 1 alpha     0.376  0.369 0.215 0.214 -0.0365  0.800  1.00    4221.    3303.
-#> 2 beta[1]  -0.665 -0.659 0.247 0.241 -1.17   -0.200  1.00    4093.    2988.
-#> 3 beta[2]  -0.275 -0.271 0.224 0.221 -0.720   0.155  1.00    4052.    3350.
-#> 4 beta[3]   0.676  0.669 0.268 0.268  0.172   1.23   1.00    4213.    2892.
+#> 1 alpha     0.382  0.381 0.221 0.222 -0.0413  0.821  1.00    3982.    3124.
+#> 2 beta[1]  -0.665 -0.661 0.252 0.253 -1.18   -0.185  1.00    4131.    3207.
+#> 3 beta[2]  -0.274 -0.280 0.226 0.226 -0.715   0.168  1.00    3816.    2736.
+#> 4 beta[3]   0.675  0.673 0.268 0.265  0.170   1.20   1.00    3963.    3257.
 
 # the functions need to calculate the appropriate
 # value for a matrix input
diff --git a/docs/reference/fit-method-time.html b/docs/reference/fit-method-time.html
index 474e4a0b4..c0a070b81 100644
--- a/docs/reference/fit-method-time.html
+++ b/docs/reference/fit-method-time.html
@@ -140,36 +140,36 @@ 
    Examples
    
 fit_mcmc <- cmdstanr_example("logistic", method = "sample")
 fit_mcmc$time()
 #> $total
-#> [1] 0.5498919
+#> [1] 0.554847
 #> 
 #> $chains
 #>   chain_id warmup sampling total
 #> 1        1  0.023    0.071 0.094
-#> 2        2  0.023    0.070 0.093
-#> 3        3  0.023    0.074 0.097
-#> 4        4  0.023    0.074 0.097
+#> 2        2  0.022    0.066 0.088
+#> 3        3  0.023    0.071 0.094
+#> 4        4  0.023    0.071 0.094
 #> 
 
 fit_vb <- cmdstanr_example("logistic", method = "variational")
 fit_vb$time()
 #> $total
-#> [1] 0.1315289
+#> [1] 0.1302531
 #> 
 
 fit_mle <- cmdstanr_example("logistic", method = "optimize", jacobian = TRUE)
 fit_mle$time()
 #> $total
-#> [1] 0.1302979
+#> [1] 0.1320548
 #> 
 
 # use fit_mle to draw samples from laplace approximation
 fit_laplace <- cmdstanr_example("logistic", method = "laplace", mode = fit_mle)
 fit_laplace$time() # just time for drawing sample not for running optimize
 #> $total
-#> [1] 0.136251
+#> [1] 0.126792
 #> 
 fit_laplace$time()$total + fit_mle$time()$total # total time
-#> [1] 0.2665489
+#> [1] 0.2588468
 # }
 
 
    
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 1036551ce..c1a096fea 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -160,7 +160,7 @@ 
    Running CmdStan from R laplace() 
    
         
-        
    Run Stan's laplace algorithm
    
+        
    Run Stan's Laplace algorithm
    
       
           
    optimize() 
    
         
diff --git a/docs/reference/model-method-check_syntax.html b/docs/reference/model-method-check_syntax.html
index 14ad8c590..6ec6f59e1 100644
--- a/docs/reference/model-method-check_syntax.html
+++ b/docs/reference/model-method-check_syntax.html
@@ -125,7 +125,7 @@ 
    Arguments
    
     
    pedantic
    
 
    (logical) Should pedantic mode be turned on? The default is
 FALSE. Pedantic mode attempts to warn you about potential issues in your
-Stan program beyond syntax errors. For details see the Pedantic mode chapter in
+Stan program beyond syntax errors. For details see the Pedantic mode chapter in
 the Stan Reference Manual.
    
 
 
@@ -202,7 +202,7 @@ 
    Examples
    
 # pedantic mode will warn that lambda should be constrained to be positive
 # and that lambda has no prior distribution
 mod$check_syntax(pedantic = TRUE)
-#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
 #>     poisson distribution is given parameter lambda as a rate parameter
 #>     (argument 1), but lambda was not constrained to be strictly positive.
 #> Warning: The parameter lambda has no priors. This means either no prior is
diff --git a/docs/reference/model-method-compile.html b/docs/reference/model-method-compile.html
index 2ae3d9c30..e5a50034d 100644
--- a/docs/reference/model-method-compile.html
+++ b/docs/reference/model-method-compile.html
@@ -171,7 +171,7 @@ 
    Arguments
    
 
    pedantic
    
 
    (logical) Should pedantic mode be turned on? The default is
 FALSE. Pedantic mode attempts to warn you about potential issues in your
-Stan program beyond syntax errors. For details see the Pedantic mode chapter in
+Stan program beyond syntax errors. For details see the Pedantic mode chapter in
 the Stan Reference Manual. Note: to do a pedantic check for a model
 without compiling it or for a model that is already compiled the
 $check_syntax() method can be used instead.
    
@@ -303,7 +303,7 @@ 
    Examples
    
 }
 ")
 mod <- cmdstan_model(file_pedantic, pedantic = TRUE)
-#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model-11d996d1c906f.stan', line 6, column 2: Parameter
+#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model-5f0d242e1d46.stan', line 6, column 2: Parameter
 #>     sigma is given a exponential distribution, which has strictly positive
 #>     support, but sigma was not constrained to be strictly positive.
 
diff --git a/docs/reference/model-method-diagnose.html b/docs/reference/model-method-diagnose.html
index e4120b50a..9a09d3085 100644
--- a/docs/reference/model-method-diagnose.html
+++ b/docs/reference/model-method-diagnose.html
@@ -165,10 +165,10 @@ 
    Arguments
    
 write_stan_json() to write R objects to JSON files compatible with
 CmdStan.
    
 
  • A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
    • 
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See Examples.
    
 
  • A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument chain_id. For MCMC, if the function
@@ -245,11 +245,11 @@ 
    Examples
    
 
 # retrieve the gradients
 test$gradients()
-#>   param_idx     value    model finite_diff        error
-#> 1         0  0.466346   3.5478      3.5478  1.64210e-08
-#> 2         1  1.496850 -35.5646    -35.5646  6.49428e-09
-#> 3         2  1.385000 -27.1027    -27.1027 -1.36431e-08
-#> 4         3 -0.467569  14.0233     14.0233  1.05492e-08
+#>   param_idx      value     model finite_diff        error
+#> 1         0 -1.0700800  30.35590    30.35590 -8.78525e-09
+#> 2         1  0.5670250 -22.14500   -22.14500  9.99824e-09
+#> 3         2  0.0466329 -10.07860   -10.07860  1.68498e-08
+#> 4         3  1.1724800  -5.09201    -5.09201  2.79421e-10
 # }
 
 
    • 
diff --git a/docs/reference/model-method-format.html b/docs/reference/model-method-format.html
index b0a71308f..3e6ef1005 100644
--- a/docs/reference/model-method-format.html
+++ b/docs/reference/model-method-format.html
@@ -212,7 +212,7 @@ 
    Examples
    
 
 # overwrite the original file instead of just printing it
 mod$format(canonicalize = list("deprecations"), overwrite_file = TRUE)
-#> Old version of the model stored to /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_1fc88c86300d78667dd3e476a636c279.stan.bak-20231108103429.
+#> Old version of the model stored to /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_1fc88c86300d78667dd3e476a636c279.stan.bak-20231213100754.
 mod$compile()
 
 
diff --git a/docs/reference/model-method-laplace-1.png b/docs/reference/model-method-laplace-1.png
index 01b1f50d56685003de39ebee8085e0410e301791..aaf21c13209e492c2de3804a26e9db36e2cbd175 100644
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-Run Stan's laplace algorithm — model-method-laplace • cmdstanrRun Stan's Laplace algorithm — model-method-laplace • cmdstanr
   
    
     
    
-    
    Run Stan's laplace algorithm
    
+    
    Run Stan's Laplace algorithm
    
     Source:     R/model.R
     
     
    
@@ -192,10 +192,10 @@ 
    Arguments
    
 write_stan_json() to write R objects to JSON files compatible with
 CmdStan.
    
 
  • A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
    • 
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See Examples.
    
 
  • A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument chain_id. For MCMC, if the function
@@ -332,9 +332,9 @@ 
    Examples
    
 
 stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
 fit_mode <- mod$optimize(data = stan_data, jacobian = TRUE)
-#> Initial log joint probability = -6.78622 
+#> Initial log joint probability = -8.0994 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        4      -6.74802   0.000186334   5.91326e-07           1           1        7    
+#>        5      -6.74802   0.000245062   1.61833e-06           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -357,15 +357,15 @@ 
    Examples
    
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad      q5      q95
 #>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
-#> 1 lp__        -7.24  -6.98  0.677 0.309 -8.74   -6.75   
-#> 2 lp_approx__ -0.505 -0.230 0.686 0.308 -1.92   -0.00197
-#> 3 theta        0.269  0.254 0.123 0.125  0.0982  0.500  
+#> 1 lp__        -7.25  -6.97  0.724 0.305 -8.72   -6.75   
+#> 2 lp_approx__ -0.516 -0.225 0.747 0.306 -1.93   -0.00324
+#> 3 theta        0.267  0.250 0.124 0.120  0.0954  0.499  
 
 # if mode isn't specified optimize is run internally first
 fit_laplace <- mod$laplace(data = stan_data)
-#> Initial log joint probability = -6.7528 
+#> Initial log joint probability = -15.1067 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        3      -6.74802   0.000769281   2.15262e-05           1           1        6    
+#>        5      -6.74802    0.00203152   6.06231e-05           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -387,9 +387,9 @@ 
    Examples
    
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad      q5      q95
 #>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
-#> 1 lp__        -7.26  -6.95  0.755 0.289 -8.76   -6.75   
-#> 2 lp_approx__ -0.518 -0.203 0.755 0.285 -2.15   -0.00170
-#> 3 theta        0.267  0.250 0.125 0.115  0.0947  0.505  
+#> 1 lp__        -7.28  -6.98  0.782 0.316 -8.84   -6.75   
+#> 2 lp_approx__ -0.531 -0.231 0.756 0.320 -1.96   -0.00280
+#> 3 theta        0.273  0.251 0.128 0.123  0.0984  0.510  
 
 # plot approximate posterior
 bayesplot::mcmc_hist(fit_laplace$draws("theta"))
diff --git a/docs/reference/model-method-optimize.html b/docs/reference/model-method-optimize.html
index f88b0551c..78b2a042d 100644
--- a/docs/reference/model-method-optimize.html
+++ b/docs/reference/model-method-optimize.html
@@ -194,10 +194,10 @@ 
    Arguments
    
 write_stan_json() to write R objects to JSON files compatible with
 CmdStan.
    • 
 
  • A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
    • 
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See Examples.
    
 
  • A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument chain_id. For MCMC, if the function
@@ -531,12 +531,12 @@ 
    Examples
    
 #> 1 lp__        -5.00
 #> 2 theta        0.2 
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -11.84 
+#> Initial log joint probability = -7.17234 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        5      -6.74802    0.00117606   2.01092e-05           1           1        8    
+#>        5      -6.74802   0.000196979   1.66919e-07           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -569,9 +569,9 @@ 
    Examples
    
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad      q5      q95
 #>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
-#> 1 lp__        -7.23  -6.96  0.703 0.291 -8.51   -6.75   
-#> 2 lp_approx__ -0.490 -0.218 0.693 0.294 -1.85   -0.00308
-#> 3 theta        0.268  0.247 0.122 0.119  0.0992  0.495  
+#> 1 lp__        -7.26  -6.98  0.759 0.313 -8.67   -6.75   
+#> 2 lp_approx__ -0.521 -0.232 0.759 0.318 -2.07   -0.00238
+#> 3 theta        0.266  0.248 0.125 0.123  0.0975  0.496  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -580,8 +580,8 @@ 
    Examples
    
 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 8e-06 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds. 
+#> Gradient evaluation took 6e-06 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -640,6 +640,53 @@ 
    Examples
    
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -16.937625 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.488e-03   4.162e-05    1.000e+00  1.000e+00       126 -6.227e+00 -6.227e+00                   
+#> Path [1] :Best Iter: [2] ELBO (-6.211714) evaluations: (126) 
+#> Path [2] :Initial log joint density = -6.798413 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               3      -6.748e+00      3.762e-03   4.627e-05    9.519e-01  9.519e-01        76 -6.195e+00 -6.195e+00                   
+#> Path [2] :Best Iter: [3] ELBO (-6.194747) evaluations: (76) 
+#> Path [3] :Initial log joint density = -7.054440 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      2.741e-03   5.225e-05    1.000e+00  1.000e+00       101 -6.187e+00 -6.187e+00                   
+#> Path [3] :Best Iter: [4] ELBO (-6.186757) evaluations: (101) 
+#> Path [4] :Initial log joint density = -7.467690 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.049e-04   4.080e-07    1.000e+00  1.000e+00       126 -6.224e+00 -6.224e+00                   
+#> Path [4] :Best Iter: [2] ELBO (-6.213804) evaluations: (126) 
+#> Path [5] :Initial log joint density = -14.817294 
+#> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      2.041e-03   5.999e-05    1.000e+00  1.000e+00       126 -6.271e+00 -6.271e+00                   
+#> Path [5] :Best Iter: [3] ELBO (-6.188344) evaluations: (126) 
+#> Path [6] :Initial log joint density = -7.375208 
+#> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      5.530e-04   1.038e-06    1.000e+00  1.000e+00       126 -6.229e+00 -6.229e+00                   
+#> Path [6] :Best Iter: [2] ELBO (-6.203592) evaluations: (126) 
+#> Path [7] :Initial log joint density = -6.748023 
+#> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               2      -6.748e+00      9.521e-04   6.579e-07    1.000e+00  1.000e+00        51 -6.231e+00 -6.231e+00                   
+#> Path [7] :Best Iter: [2] ELBO (-6.231405) evaluations: (51) 
+#> Path [8] :Initial log joint density = -11.312958 
+#> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.017e-03   1.576e-05    1.000e+00  1.000e+00       126 -6.304e+00 -6.304e+00                   
+#> Path [8] :Best Iter: [3] ELBO (-6.185214) evaluations: (126) 
+#> Path [9] :Initial log joint density = -6.881067 
+#> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      9.878e-04   9.538e-06    1.000e+00  1.000e+00       101 -6.173e+00 -6.173e+00                   
+#> Path [9] :Best Iter: [4] ELBO (-6.173184) evaluations: (101) 
+#> Path [10] :Initial log joint density = -7.173191 
+#> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.980e-04   1.685e-07    1.000e+00  1.000e+00       126 -6.176e+00 -6.176e+00                   
+#> Path [10] :Best Iter: [5] ELBO (-6.175625) evaluations: (126) 
+#> Total log probability function evaluations:1235 
+#> Finished in  0.1 seconds.
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/docs/reference/model-method-pathfinder.html b/docs/reference/model-method-pathfinder.html
index f6423f16c..e5caa27c5 100644
--- a/docs/reference/model-method-pathfinder.html
+++ b/docs/reference/model-method-pathfinder.html
@@ -2,12 +2,15 @@
 Run Stan's Pathfinder Variational Inference Algorithm — model-method-pathfinder • cmdstanrCmdStanModel object runs
 Stan's Pathfinder algorithms. Pathfinder is a variational method for
 approximately sampling from differentiable log densities. Starting from a
-random initialization, Pathfinder locates normal approximations to the
-target density along a quasi-Newton optimization path, with local
-covariance estimated using the negative inverse Hessian estimates produced
-by the L-BFGS optimizer. Pathfinder returns draws from the Gaussian
-approximation with the lowest estimated Kullback-Leibler (KL) divergence to
-the true posterior. See the
+random initialization, Pathfinder locates normal approximations
+to the target density along a quasi-Newton optimization path in
+the unconstrained space, with local covariance estimated using
+the negative inverse Hessian estimates produced by the LBFGS
+optimizer. Pathfinder selects the normal approximation with the
+lowest estimated Kullback-Leibler (KL) divergence to the true
+posterior. Finally Pathfinder draws from that normal
+approximation and returns the draws transformed to the
+constrained scale. See the
 CmdStan User’s Guide
 for more details.
    
 
    Any argument left as NULL will default to the default value used by the
@@ -203,10 +209,10 @@ 
    Arguments
    
 write_stan_json() to write R objects to JSON files compatible with
 CmdStan.
    • 
 
  • A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
    • 
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See Examples.
    
 
  • A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument chain_id. For MCMC, if the function
@@ -310,8 +316,9 @@ 
    Arguments
    
 
 
    draws
    
 
    (positive integer) Number of draws to return after performing
-pareto smooted importance sampling (PSIS). This must be smaller than
-single_path_draws * num_paths.
    
+pareto smooted importance sampling (PSIS). This should be smaller than
+single_path_draws * num_paths (future versions of CmdStan will throw a
+warning).
    
 
 
 
    num_paths
    
@@ -548,12 +555,12 @@ 
    Examples
    
 #> 1 lp__        -5.00
 #> 2 theta        0.2 
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -8.53772 
+#> Initial log joint probability = -11.5709 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        5      -6.74802   0.000313669   2.44314e-06           1           1        8    
+#>        5      -6.74802    0.00107647   1.72219e-05           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -584,11 +591,11 @@ 
    Examples
    
 #> Finished in  0.1 seconds.
 fit_laplace$summary()
 #> # A tibble: 3 × 7
-#>   variable      mean median    sd   mad      q5      q95
-#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
-#> 1 lp__        -7.24  -6.97  0.690 0.299 -8.66   -6.75   
-#> 2 lp_approx__ -0.502 -0.223 0.693 0.303 -1.94   -0.00213
-#> 3 theta        0.267  0.247 0.123 0.119  0.0995  0.501  
+#>   variable      mean median    sd   mad     q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
+#> 1 lp__        -7.23  -6.99  0.637 0.323 -8.54  -6.75   
+#> 2 lp_approx__ -0.486 -0.238 0.644 0.323 -1.80  -0.00166
+#> 3 theta        0.268  0.252 0.121 0.126  0.104  0.492  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -597,8 +604,8 @@ 
    Examples
    
 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 7e-06 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds. 
+#> Gradient evaluation took 6e-06 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -657,6 +664,53 @@ 
    Examples
    
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -14.470660 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      2.026e-03   5.797e-05    1.000e+00  1.000e+00       126 -6.259e+00 -6.259e+00                   
+#> Path [1] :Best Iter: [4] ELBO (-6.211388) evaluations: (126) 
+#> Path [2] :Initial log joint density = -9.064681 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      3.457e-04   2.927e-06    1.000e+00  1.000e+00       126 -6.253e+00 -6.253e+00                   
+#> Path [2] :Best Iter: [3] ELBO (-6.227371) evaluations: (126) 
+#> Path [3] :Initial log joint density = -15.152638 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      2.028e-03   6.063e-05    1.000e+00  1.000e+00       126 -6.230e+00 -6.230e+00                   
+#> Path [3] :Best Iter: [4] ELBO (-6.211247) evaluations: (126) 
+#> Path [4] :Initial log joint density = -10.220491 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      7.043e-04   8.900e-06    1.000e+00  1.000e+00       126 -6.194e+00 -6.194e+00                   
+#> Path [4] :Best Iter: [5] ELBO (-6.193620) evaluations: (126) 
+#> Path [5] :Initial log joint density = -6.766009 
+#> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               3      -6.748e+00      2.724e-03   1.458e-04    1.000e+00  1.000e+00        76 -6.232e+00 -6.232e+00                   
+#> Path [5] :Best Iter: [3] ELBO (-6.232284) evaluations: (76) 
+#> Path [6] :Initial log joint density = -7.304442 
+#> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      4.014e-04   5.879e-07    1.000e+00  1.000e+00       126 -6.247e+00 -6.247e+00                   
+#> Path [6] :Best Iter: [4] ELBO (-6.212233) evaluations: (126) 
+#> Path [7] :Initial log joint density = -7.233791 
+#> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      2.804e-04   3.115e-07    1.000e+00  1.000e+00       126 -6.275e+00 -6.275e+00                   
+#> Path [7] :Best Iter: [4] ELBO (-6.122234) evaluations: (126) 
+#> Path [8] :Initial log joint density = -11.942008 
+#> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.223e-03   2.161e-05    1.000e+00  1.000e+00       126 -6.232e+00 -6.232e+00                   
+#> Path [8] :Best Iter: [3] ELBO (-6.217220) evaluations: (126) 
+#> Path [9] :Initial log joint density = -7.438064 
+#> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      9.817e-05   3.665e-07    1.000e+00  1.000e+00       126 -6.268e+00 -6.268e+00                   
+#> Path [9] :Best Iter: [3] ELBO (-6.213533) evaluations: (126) 
+#> Path [10] :Initial log joint density = -8.992806 
+#> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      3.448e-04   2.904e-06    1.000e+00  1.000e+00       126 -6.247e+00 -6.247e+00                   
+#> Path [10] :Best Iter: [2] ELBO (-6.141839) evaluations: (126) 
+#> Total log probability function evaluations:1360 
+#> Finished in  0.1 seconds.
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/docs/reference/model-method-sample.html b/docs/reference/model-method-sample.html
index 02207960e..7925e4b45 100644
--- a/docs/reference/model-method-sample.html
+++ b/docs/reference/model-method-sample.html
@@ -210,10 +210,10 @@ 
    Arguments
    
 write_stan_json() to write R objects to JSON files compatible with
 CmdStan.
    • 
 
  • A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
    • 
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See Examples.
    
 
  • A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument chain_id. For MCMC, if the function
@@ -659,12 +659,12 @@ 
    Examples
    
 #> 1 lp__        -5.00
 #> 2 theta        0.2 
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.01351 
+#> Initial log joint probability = -11.0695 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        4      -6.74802   0.000544739   0.000133582      0.9055      0.9055        7    
+#>        5      -6.74802   0.000954615   1.42918e-05           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -695,11 +695,11 @@ 
    Examples
    
 #> Finished in  0.1 seconds.
 fit_laplace$summary()
 #> # A tibble: 3 × 7
-#>   variable      mean median    sd   mad      q5      q95
-#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
-#> 1 lp__        -7.24  -6.98  0.692 0.314 -8.61   -6.75   
-#> 2 lp_approx__ -0.500 -0.233 0.688 0.317 -1.89   -0.00172
-#> 3 theta        0.269  0.248 0.123 0.122  0.0989  0.503  
+#>   variable      mean median    sd   mad     q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
+#> 1 lp__        -7.23  -6.97  0.710 0.297 -8.57  -6.75   
+#> 2 lp_approx__ -0.490 -0.220 0.696 0.300 -1.90  -0.00171
+#> 3 theta        0.270  0.252 0.123 0.120  0.102  0.495  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -708,8 +708,8 @@ 
    Examples
    
 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 7e-06 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds. 
+#> Gradient evaluation took 6e-06 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -768,6 +768,53 @@ 
    Examples
    
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -10.398597 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      7.601e-04   1.002e-05    1.000e+00  1.000e+00       126 -6.246e+00 -6.246e+00                   
+#> Path [1] :Best Iter: [4] ELBO (-6.213236) evaluations: (126) 
+#> Path [2] :Initial log joint density = -7.343771 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      5.658e-03   1.740e-04    1.000e+00  1.000e+00       101 -6.270e+00 -6.270e+00                   
+#> Path [2] :Best Iter: [3] ELBO (-6.236555) evaluations: (101) 
+#> Path [3] :Initial log joint density = -7.253954 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      4.780e-03   1.316e-04    1.000e+00  1.000e+00       101 -6.293e+00 -6.293e+00                   
+#> Path [3] :Best Iter: [3] ELBO (-6.210231) evaluations: (101) 
+#> Path [4] :Initial log joint density = -6.884985 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      1.025e-03   1.015e-05    1.000e+00  1.000e+00       101 -6.254e+00 -6.254e+00                   
+#> Path [4] :Best Iter: [4] ELBO (-6.254382) evaluations: (101) 
+#> Path [5] :Initial log joint density = -10.549385 
+#> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      8.063e-04   1.099e-05    1.000e+00  1.000e+00       126 -6.155e+00 -6.155e+00                   
+#> Path [5] :Best Iter: [5] ELBO (-6.154812) evaluations: (126) 
+#> Path [6] :Initial log joint density = -11.021697 
+#> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      9.418e-04   1.400e-05    1.000e+00  1.000e+00       126 -6.261e+00 -6.261e+00                   
+#> Path [6] :Best Iter: [4] ELBO (-6.204645) evaluations: (126) 
+#> Path [7] :Initial log joint density = -10.721189 
+#> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      8.573e-04   1.209e-05    1.000e+00  1.000e+00       126 -6.195e+00 -6.195e+00                   
+#> Path [7] :Best Iter: [5] ELBO (-6.194637) evaluations: (126) 
+#> Path [8] :Initial log joint density = -11.729882 
+#> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.125e-03   1.854e-05    1.000e+00  1.000e+00       126 -6.195e+00 -6.195e+00                   
+#> Path [8] :Best Iter: [4] ELBO (-6.193964) evaluations: (126) 
+#> Path [9] :Initial log joint density = -8.375820 
+#> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      2.924e-04   2.169e-06    1.000e+00  1.000e+00       126 -6.198e+00 -6.198e+00                   
+#> Path [9] :Best Iter: [2] ELBO (-6.188312) evaluations: (126) 
+#> Path [10] :Initial log joint density = -7.041152 
+#> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      2.604e-03   4.795e-05    1.000e+00  1.000e+00       101 -6.233e+00 -6.233e+00                   
+#> Path [10] :Best Iter: [2] ELBO (-6.214834) evaluations: (101) 
+#> Total log probability function evaluations:1310 
+#> Finished in  0.1 seconds.
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/docs/reference/model-method-sample_mpi.html b/docs/reference/model-method-sample_mpi.html
index 5dd06c42b..6247b07ac 100644
--- a/docs/reference/model-method-sample_mpi.html
+++ b/docs/reference/model-method-sample_mpi.html
@@ -1,7 +1,7 @@
 
 Run Stan's MCMC algorithms with MPI — model-method-sample_mpi • cmdstanr
     
    
     
    The $sample_mpi() method of a CmdStanModel object is
 identical to the $sample() method but with support for
-MPI. The target audience for MPI are
+MPI (message passing interface). The target audience for MPI are
 those with large computer clusters. For other users, the
 $sample() method provides both parallelization of
 chains and threading support for within-chain parallelization.
    
@@ -247,10 +247,10 @@ 
    Arguments
    
 write_stan_json() to write R objects to JSON files compatible with
 CmdStan.
    • 
 
  • A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
    • 
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See Examples.
    
 
  • A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument chain_id. For MCMC, if the function
@@ -467,8 +467,8 @@ 
    See also
    
 documentation and tutorials.
    
 
    The Stan and CmdStan documentation:
    The Stan Math Library's MPI documentation
-(mc-stan.org/math/mpi) for more
+
    The Stan Math Library's documentation
+(mc-stan.org/math) for more
 details on MPI support in Stan.
    
 
    Other CmdStanModel methods: 
 model-method-check_syntax,
diff --git a/docs/reference/model-method-variational.html b/docs/reference/model-method-variational.html
index 7aee6bceb..ed9fcbc43 100644
--- a/docs/reference/model-method-variational.html
+++ b/docs/reference/model-method-variational.html
@@ -197,10 +197,10 @@ 
    Arguments
    
 write_stan_json() to write R objects to JSON files compatible with
 CmdStan.
    • 
 
  • A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
    • 
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See Examples.
    
 
  • A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument chain_id. For MCMC, if the function
@@ -534,12 +534,12 @@ 
    Examples
    
 #> 1 lp__        -5.00
 #> 2 theta        0.2 
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.23249 
+#> Initial log joint probability = -11.7793 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        4      -6.74802    0.00456563   0.000121975           1           1        7    
+#>        5      -6.74802    0.00114772   1.92382e-05           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -572,9 +572,9 @@ 
    Examples
    
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad     q5      q95
 #>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
-#> 1 lp__        -7.25  -6.99  0.712 0.326 -8.66  -6.75   
-#> 2 lp_approx__ -0.506 -0.244 0.715 0.329 -1.94  -0.00187
-#> 3 theta        0.269  0.250 0.124 0.126  0.101  0.500  
+#> 1 lp__        -7.22  -6.96  0.669 0.294 -8.51  -6.75   
+#> 2 lp_approx__ -0.470 -0.213 0.645 0.295 -1.81  -0.00204
+#> 3 theta        0.267  0.247 0.121 0.118  0.104  0.493  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -583,8 +583,8 @@ 
    Examples
    
 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 7e-06 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds. 
+#> Gradient evaluation took 5e-06 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -643,6 +643,54 @@ 
    Examples
    
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -7.711026 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.614e-04   8.188e-07    1.000e+00  1.000e+00       126 -6.206e+00 -6.206e+00                   
+#> Path [1] :Best Iter: [5] ELBO (-6.205511) evaluations: (126) 
+#> Path [2] :Initial log joint density = -12.450513 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.452e-03   2.960e-05    1.000e+00  1.000e+00       126 -6.251e+00 -6.251e+00                   
+#> Path [2] :Best Iter: [4] ELBO (-6.229238) evaluations: (126) 
+#> Path [3] :Initial log joint density = -7.647405 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.466e-04   7.009e-07    1.000e+00  1.000e+00       126 -6.193e+00 -6.193e+00                   
+#> Path [3] :Best Iter: [3] ELBO (-6.164986) evaluations: (126) 
+#> Path [4] :Initial log joint density = -15.036045 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      2.036e-03   6.056e-05    1.000e+00  1.000e+00       126 -6.235e+00 -6.235e+00                   
+#> Path [4] :Best Iter: [2] ELBO (-6.225650) evaluations: (126) 
+#> Path [5] :Initial log joint density = -6.760365 
+#> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               3      -6.748e+00      1.911e-03   8.518e-05    1.000e+00  1.000e+00        76 -6.247e+00 -6.247e+00                   
+#> Path [5] :Best Iter: [3] ELBO (-6.246721) evaluations: (76) 
+#> Path [6] :Initial log joint density = -6.826898 
+#> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               3      -6.748e+00      5.423e-03   1.282e-04    9.360e-01  9.360e-01        76 -6.169e+00 -6.169e+00                   
+#> Path [6] :Best Iter: [3] ELBO (-6.168890) evaluations: (76) 
+#> Path [7] :Initial log joint density = -8.134235 
+#> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      2.517e-04   1.691e-06    1.000e+00  1.000e+00       126 -6.243e+00 -6.243e+00                   
+#> Path [7] :Best Iter: [3] ELBO (-6.175493) evaluations: (126) 
+#> Path [8] :Initial log joint density = -7.511494 
+#> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      9.446e-04   2.687e-06    1.000e+00  1.000e+00       126 -6.214e+00 -6.214e+00                   
+#> Path [8] :Best Iter: [2] ELBO (-6.197279) evaluations: (126) 
+#> Path [9] :Initial log joint density = -11.516990 
+#> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.065e-03   1.693e-05    1.000e+00  1.000e+00       126 -6.244e+00 -6.244e+00                   
+#> Path [9] :Best Iter: [3] ELBO (-6.194623) evaluations: (126) 
+#> Path [10] :Initial log joint density = -17.778190 
+#> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.013e-03   2.414e-05    1.000e+00  1.000e+00       126 -6.262e+00 -6.262e+00                   
+#> Path [10] :Best Iter: [4] ELBO (-6.170410) evaluations: (126) 
+#> Total log probability function evaluations:1310 
+#> Pareto k value (0.820024) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor. 
+#> Finished in  0.1 seconds.
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/docs/reference/read_cmdstan_csv.html b/docs/reference/read_cmdstan_csv.html
index d5b0c50d8..d6b8fb744 100644
--- a/docs/reference/read_cmdstan_csv.html
+++ b/docs/reference/read_cmdstan_csv.html
@@ -223,10 +223,10 @@ 
    Examples
    
 fit1 <- cmdstanr_example("logistic", method = "sample", save_warmup = TRUE)
 csv_files <- fit1$output_files()
 print(csv_files)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-1-32a06f.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-2-32a06f.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-3-32a06f.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-4-32a06f.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-1-642e8e.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-2-642e8e.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-3-642e8e.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-4-642e8e.csv"
 
 # Creating fitting model objects
 
@@ -234,9 +234,9 @@ 
    Examples
    
 fit2 <- as_cmdstan_fit(csv_files)
 fit2$print("beta")
 #>  variable  mean median   sd  mad    q5   q95 rhat ess_bulk ess_tail
-#>   beta[1] -0.67  -0.66 0.25 0.24 -1.08 -0.28 1.00     4402     2852
-#>   beta[2] -0.27  -0.27 0.22 0.22 -0.65  0.09 1.00     4396     2793
-#>   beta[3]  0.68   0.67 0.27 0.26  0.27  1.12 1.00     4545     3039
+#>   beta[1] -0.67  -0.66 0.25 0.25 -1.09 -0.26 1.00     4234     3123
+#>   beta[2] -0.28  -0.27 0.23 0.23 -0.65  0.09 1.00     4067     3185
+#>   beta[3]  0.69   0.67 0.27 0.27  0.26  1.14 1.00     4291     3208
 
 # Using read_cmdstan_csv
 #
@@ -248,7 +248,7 @@ 
    Examples
    
 #>   ..$ stan_version_major  : num 2
 #>   ..$ stan_version_minor  : num 33
 #>   ..$ stan_version_patch  : num 0
-#>   ..$ start_datetime      : chr "2023-11-08 17:35:02 UTC"
+#>   ..$ start_datetime      : chr "2023-12-13 17:08:27 UTC"
 #>   ..$ method              : chr "sample"
 #>   ..$ save_warmup         : num 1
 #>   ..$ thin                : num 1
@@ -265,14 +265,14 @@ 
    Examples
    
 #>   ..$ num_chains          : num 1
 #>   ..$ id                  : num [1:4] 1 2 3 4
 #>   ..$ init                : num [1:4] 2 2 2 2
-#>   ..$ seed                : num 1.63e+09
+#>   ..$ seed                : num 1.1e+09
 #>   ..$ refresh             : num 100
 #>   ..$ sig_figs            : num -1
-#>   ..$ profile_file        : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081035-1-02259b.csv"
+#>   ..$ profile_file        : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131008-1-0e2cb1.csv"
 #>   ..$ stanc_version       : chr "stanc3 v2.33.1"
 #>   ..$ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
 #>   ..$ variables           : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#>   ..$ step_size_adaptation: num [1:4] 0.731 0.842 0.658 0.727
+#>   ..$ step_size_adaptation: num [1:4] 0.761 0.758 0.859 0.765
 #>   ..$ model_name          : chr "logistic_model"
 #>   ..$ adapt_engaged       : num 1
 #>   ..$ adapt_delta         : num 0.8
@@ -283,9 +283,9 @@ 
    Examples
    
 #>   ..$ threads_per_chain   : num 1
 #>   ..$ time                :'data.frame':	4 obs. of  4 variables:
 #>   .. ..$ chain_id: num [1:4] 1 2 3 4
-#>   .. ..$ warmup  : num [1:4] 0.071 0.073 0.078 0.075
-#>   .. ..$ sampling: num [1:4] 0.075 0.089 0.078 0.073
-#>   .. ..$ total   : num [1:4] 0.146 0.162 0.156 0.148
+#>   .. ..$ warmup  : num [1:4] 0.071 0.073 0.072 0.069
+#>   .. ..$ sampling: num [1:4] 0.068 0.068 0.065 0.068
+#>   .. ..$ total   : num [1:4] 0.139 0.141 0.137 0.137
 #>   ..$ stan_variable_sizes :List of 4
 #>   .. ..$ lp__   : num 1
 #>   .. ..$ alpha  : num 1
@@ -297,35 +297,35 @@ 
    Examples
    
 #>   ..$ total : int NA
 #>   ..$ chains:'data.frame':	4 obs. of  4 variables:
 #>   .. ..$ chain_id: num [1:4] 1 2 3 4
-#>   .. ..$ warmup  : num [1:4] 0.071 0.073 0.078 0.075
-#>   .. ..$ sampling: num [1:4] 0.075 0.089 0.078 0.073
-#>   .. ..$ total   : num [1:4] 0.146 0.162 0.156 0.148
+#>   .. ..$ warmup  : num [1:4] 0.071 0.073 0.072 0.069
+#>   .. ..$ sampling: num [1:4] 0.068 0.068 0.065 0.068
+#>   .. ..$ total   : num [1:4] 0.139 0.141 0.137 0.137
 #>  $ inv_metric                     :List of 4
-#>   ..$ 1: num [1:4] 0.0464 0.0532 0.0478 0.0741
-#>   ..$ 2: num [1:4] 0.0374 0.0624 0.0482 0.0688
-#>   ..$ 3: num [1:4] 0.042 0.0545 0.0473 0.0754
-#>   ..$ 4: num [1:4] 0.0411 0.0563 0.0539 0.0609
+#>   ..$ 1: num [1:4] 0.0469 0.0641 0.0523 0.0688
+#>   ..$ 2: num [1:4] 0.0438 0.0555 0.0497 0.0685
+#>   ..$ 3: num [1:4] 0.0457 0.0612 0.0451 0.0706
+#>   ..$ 4: num [1:4] 0.0436 0.0627 0.0506 0.0832
 #>  $ step_size                      :List of 4
-#>   ..$ 1: num 0.731
-#>   ..$ 2: num 0.842
-#>   ..$ 3: num 0.658
-#>   ..$ 4: num 0.727
-#>  $ warmup_draws                   : 'draws_array' num [1:1000, 1:4, 1:105] -78.1 -78.1 -78.1 -64.4 -66.7 ...
+#>   ..$ 1: num 0.761
+#>   ..$ 2: num 0.758
+#>   ..$ 3: num 0.859
+#>   ..$ 4: num 0.765
+#>  $ warmup_draws                   : 'draws_array' num [1:1000, 1:4, 1:105] -72 -72 -72 -71.3 -68.2 ...
 #>   ..- attr(*, "dimnames")=List of 3
 #>   .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
 #>   .. ..$ chain    : chr [1:4] "1" "2" "3" "4"
 #>   .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#>  $ post_warmup_draws              : 'draws_array' num [1:1000, 1:4, 1:105] -68.5 -66.2 -68 -68.3 -67.2 ...
+#>  $ post_warmup_draws              : 'draws_array' num [1:1000, 1:4, 1:105] -64.3 -64.3 -65.7 -67.2 -66 ...
 #>   ..- attr(*, "dimnames")=List of 3
 #>   .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
 #>   .. ..$ chain    : chr [1:4] "1" "2" "3" "4"
 #>   .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#>  $ warmup_sampler_diagnostics     : 'draws_array' num [1:1000, 1:4, 1:6] 1 0 0 1 0.515 ...
+#>  $ warmup_sampler_diagnostics     : 'draws_array' num [1:1000, 1:4, 1:6] 1 0 0 0.597 1 ...
 #>   ..- attr(*, "dimnames")=List of 3
 #>   .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
 #>   .. ..$ chain    : chr [1:4] "1" "2" "3" "4"
 #>   .. ..$ variable : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
-#>  $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.856 1 0.856 0.984 1 ...
+#>  $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.971 0.97 0.742 0.882 0.87 ...
 #>   ..- attr(*, "dimnames")=List of 3
 #>   .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
 #>   .. ..$ chain    : chr [1:4] "1" "2" "3" "4"
diff --git a/docs/reference/write_stan_file.html b/docs/reference/write_stan_file.html
index 10ffeb6e3..e14591323 100644
--- a/docs/reference/write_stan_file.html
+++ b/docs/reference/write_stan_file.html
@@ -188,7 +188,7 @@ 
    Examples
    
 
 f <- write_stan_file(stan_program)
 print(f)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_7f12fc190dd23b0e462f7d73040dd97e.stan"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_7f12fc190dd23b0e462f7d73040dd97e.stan"
 
 lines <- readLines(f)
 print(lines)
diff --git a/docs/reference/write_stan_json.html b/docs/reference/write_stan_json.html
index d09ca22cc..b7b128ef1 100644
--- a/docs/reference/write_stan_json.html
+++ b/docs/reference/write_stan_json.html
@@ -167,13 +167,13 @@ 
    Examples
    
 #>   "N": 5,
 #>   "K": 2,
 #>   "x": [
-#>     [0.539316345307751, 1.20165111819522],
-#>     [0.027826781785157, -0.219847554976374],
-#>     [-0.171509078741447, 1.08053600860179],
-#>     [1.01158266854045, 0.0611935712707024],
-#>     [-0.426238537104135, 0.270940591850318]
+#>     [0.281077678546936, -1.18908686575029],
+#>     [0.858726751146771, 0.46461805175525],
+#>     [-0.700205759803986, -0.115709696625946],
+#>     [1.45853659080418, 0.995745564145492],
+#>     [-0.671485199795285, -0.298752493529616]
 #>   ],
-#>   "y": [10, 8, 13, 10, 12],
+#>   "y": [12, 6, 11, 9, 11],
 #>   "z": [1, 0]
 #> }
 
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 6866e0562..74c961502 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -3,6 +3,9 @@
   
     https://mc-stan.org/cmdstanr/404.html
   
+  
+    https://mc-stan.org/cmdstanr/CONTRIBUTING.html
+  
   
     https://mc-stan.org/cmdstanr/LICENSE-text.html
   

From b32e42e97cd1597124edfc111b97b1182db239d7 Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Wed, 13 Dec 2023 13:21:53 -0700
Subject: [PATCH 2/2] update NEWS

---
 NEWS.md              | 3 +++
 docs/news/index.html | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 79d1205d1..6f56b0cf7 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -19,6 +19,9 @@
 * Add support/tests for exposing functions with tuples by @andrjohns in #860
 * Add support/tests for exporting functions with complex types by @andrjohns in #861
 * Add option for installing from release archive by @andrjohns in #866
+* Improve Pathfinder doc by @avehtari in #875
+* Rename `jacobian_adjustment` argument to `jacobian` by @jgabry in #879
+* Fix get_cmdstan_flags('STANCFLAGS') in recursive make by @pearsonca in #881
 
 # cmdstanr 0.6.1
 
diff --git a/docs/news/index.html b/docs/news/index.html
index 4dbb505ff..f702a0449 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -123,6 +123,9 @@ 
    Other improvements and bug fixes
 
  • Add support/tests for exposing functions with tuples by @andrjohns in #860
    • 
 
  • Add support/tests for exporting functions with complex types by @andrjohns in #861
    • 
 
  • Add option for installing from release archive by @andrjohns in #866
    • 
+
  • Improve Pathfinder doc by @avehtari in #875
    • 
+
  • Rename jacobian_adjustment argument to jacobian by @jgabry in #879
    • 
+
  • Fix get_cmdstan_flags(‘STANCFLAGS’) in recursive make by @pearsonca in #881
    • 
 
    •