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remove mentions of rstan::read_stan_csv
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jgabry committed Jul 31, 2024
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5 changes: 0 additions & 5 deletions R/model.R
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Expand Up @@ -85,11 +85,6 @@
#' # Plot posterior using bayesplot (ggplot2)
#' mcmc_hist(fit_mcmc$draws("theta"))
#'
#' # For models fit using MCMC, if you like working with RStan's stanfit objects
#' # then you can create one with rstan::read_stan_csv()
#' # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())
#'
#'
#' # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm)
#' # and also demonstrate specifying data as a path to a file instead of a list
#' my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json")
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5 changes: 0 additions & 5 deletions man/CmdStanModel.Rd

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5 changes: 0 additions & 5 deletions man/cmdstan_model.Rd

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16 changes: 0 additions & 16 deletions vignettes/cmdstanr.Rmd
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Expand Up @@ -326,22 +326,6 @@ CmdStan itself provides a `diagnose` utility that can be called using
the `$cmdstan_diagnose()` method. This method will print warnings but won't return anything.


### Create a `stanfit` object

If you have RStan installed then it is also possible to create a `stanfit`
object from the csv output files written by CmdStan. This can be done by using
`rstan::read_stan_csv()` in combination with the `$output_files()` method of the
`CmdStanMCMC` object. This is only needed if you want to fit a model with
CmdStanR but already have a lot of post-processing code that assumes a `stanfit`
object. Otherwise we recommend using the post-processing functionality provided
by CmdStanR itself.

```{r stanfit, eval=FALSE}
stanfit <- rstan::read_stan_csv(fit$output_files())
```



## Running optimization and variational inference

CmdStanR also supports running Stan's optimization algorithms and its algorithms
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