v1.1.0-0

• More stable SAEM/FOCEi algorithms
• More robust estimation with FOCEi
• Support of NONMEM-style datasets directly (except MDV and some other less used data items)
• Align nlme/SAEM/FOCEi objective functions to be similar in value (and close or equal to NONMEM FOCEi objective function)
• Align nlme/SAEM/FOCEi likelihood and likelihood measurments
• Add model piping and a new model piping vignette
• Add support for lag(), rate(), dur() and f() without between subject variability for all supported estimation methods

FOCEi pre-release

• FOCEi standard errors have been made more accurate

CRAN release

v1.0.0-7

Merge remote-tracking branch 'origin/master'

VPC fixes

Update DESCRIPTION

Version bump

Multiple Endpoint FO/FOi

A comprehensive collection of non-linear mixed effect (nlme) model algorithms:

• First Order Estimation (FO) with FOCE post-hoc etas

## eg
nlmixr(modelfn, data, "fo"); # has FOCE post-hoc ETAs

• First Order Estimation (FO) with FOCEi post-hoc ETAs

## eg
nlmixr(modelfn, data, "foi"); # has FOCEi post-hoc ETAs

• First Order Conditional Estimate (FOCE)

## eg
nlmixr(modelfn, data, "foce");

• First Order Conditional Estimate with interaction (FOCEi),

## eg
nlmixr(modelfn, data, "focei");

• Adaptive Gaussian quadrature (with Laplacian approximation as a special case see glmm and glmm2),
• Stochastic Approximation Estimation-Maximization (SAEM).

## eg
nlmixr(modelfn, data, "saem")

• Traditional R nlme

## eg
nlmixr(modelfn, data, "nlme")

Other features

• A minimalist, intuitive, expressive, and domain-specific nlme modeling language.
• The capability of joint modeling of multiple endpoints.
• A revamped SAEM engine with improved speed and stability.
• The capability of out-of-box Visual Predictive Checks (VPC) after a model fit.
• The capability of out-of-box sophisticated Clinical Trial Simulation (CTS) after a model fit.
• The capability of an out-of-box comprehensive diagnostic kit with a direct hook to xpose after a model fit.
• The capability of modeling “odd type” data, including binary data, count data, and bounded clinical endpoint (e.g., ADAS-cog has a range of 0 to 70).
• Parallel computing ODE solving via the openmp package -- an industry's first among the current population PK/PD simulators to the best of our knowledge.
• An intuitive, powerful, graphic user interface (GUI) based project manager in shinyMixR.

New in this release

This added a few things

• fo and foi estimation routines
• Multiple endpoint UI support:
• SAEM bugfix

## Error in model block
## cp1 is modeled by additive error for compartment depot
cp1 ~ add(add.err1) | depot
cp2 ~ add(add.err2) | central

Add FO/FOi and multiple endpoint UI support.

A comprehensive collection of non-linear mixed effect (nlme) model algorithms:

• First Order Estimation (FO) with FOCE post-hoc etas

## eg
nlmixr(modelfn, data, "fo"); # has FOCE post-hoc ETAs

• First Order Estimation (FO) with FOCEi post-hoc ETAs

## eg
nlmixr(modelfn, data, "foi"); # has FOCEi post-hoc ETAs

• First Order Conditional Estimate (FOCE)

## eg
nlmixr(modelfn, data, "foce");

• First Order Conditional Estimate with interaction (FOCEi),

## eg
nlmixr(modelfn, data, "focei");

• Adaptive Gaussian quadrature (with Laplacian approximation as a special case see glmm and glmm2),
• Stochastic Approximation Estimation-Maximization (SAEM).

## eg
nlmixr(modelfn, data, "saem")

• Traditional R nlme

## eg
nlmixr(modelfn, data, "nlme")

Other features

• A minimalist, intuitive, expressive, and domain-specific nlme modeling language.
• The capability of joint modeling of multiple endpoints.
  -c A revamped SAEM engine with improved speed and stability.
• The capability of out-of-box Visual Predictive Checks (VPC) after a model fit.
• The capability of out-of-box sophisticated Clinical Trial Simulation (CTS) after a model fit.
• The capability of an out-of-box comprehensive diagnostic kit with a direct hook to xpose after a model fit.
• The capability of modeling “odd type” data, including binary data, count data, and bounded clinical endpoint (e.g., ADAS-cog has a range of 0 to 70).
• Parallel computing ODE solving via the openmp package -- an industry's first among the current population PK/PD simulators to the best of our knowledge.
• An intuitive, powerful, graphic user interface (GUI) based project manager in shinyMixR.

New in this release

This added a few things

• fo and foi estimation routines
• Multiple endpoint UI support:

## Error in model block
## cp1 is modeled by additive error for compartment depot
cp1 ~ add(add.err1) | depot
cp2 ~ add(add.err2) | central

SAEM and FOCEi stabilization

A comprehensive collection of non-linear mixed effect (nlme) model algorithms:

• First Order Conditional Estimate (FOCE)

## eg
nlmixr(modelfn, data, "foce");

• First Order Conditional Estimate with interaction (FOCEi),

## eg
nlmixr(modelfn, data, "focei");

• Adaptive Gaussian quadrature (with Laplacian approximation as a special case see glmm and glmm2),
• Stochastic Approximation Estimation-Maximization (SAEM).

## eg
nlmixr(modelfn, data, "saem")

• Traditional R nlme

## eg
nlmixr(modelfn, data, "nlme")

Other features

• A minimalist, intuitive, expressive, and domain-specific nlme modeling language.
• The capability of joint modeling of multiple endpoints.
• A revamped SAEM engine with improved speed and stability.
• The capability of out-of-box Visual Predictive Checks (VPC) after a model fit.
• The capability of out-of-box sophisticated Clinical Trial Simulation (CTS) after a model fit.
• The capability of an out-of-box comprehensive diagnostic kit with a direct hook to xpose after a model fit.
• The capability of modeling “odd type” data, including binary data, count data, and bounded clinical endpoint (e.g., ADAS-cog has a range of 0 to 70).
• Parallel computing ODE solving via the openmp package -- an industry's first among the current population PK/PD simulators to the best of our knowledge.
• An intuitive, powerful, graphic user interface (GUI) based project manager in shinyMixR.

New in this release

This addresses a few stablitily fixes in the last release

• SAEM FOCEi objective function was fixed
• Standard Errors on FOCEi are more reasonable

v1.0.0-2

A comprehensive collection of non-linear mixed effect (nlme) model algorithms:

• First Order Conditional Estimate (FOCE)

## eg
nlmixr(modelfn, data, "foce");

• First Order Conditional Estimate with interaction (FOCEi),

## eg
nlmixr(modelfn, data, "focei");

• Adaptive Gaussian quadrature (with Laplacian approximation as a special case see glmm and glmm2),
• Stochastic Approximation Estimation-Maximization (SAEM).

## eg
nlmixr(modelfn, data, "saem")

• Traditional R nlme

## eg
nlmixr(modelfn, data, "nlme")

Other features

• A minimalist, intuitive, expressive, and domain-specific nlme modeling language.
• The capability of joint modeling of multiple endpoints.
• A revamped SAEM engine with improved speed and stability.
• The capability of out-of-box Visual Predictive Checks (VPC) after a model fit.
• The capability of out-of-box sophisticated Clinical Trial Simulation (CTS) after a model fit.
• The capability of an out-of-box comprehensive diagnostic kit with a direct hook to xpose after a model fit.
• The capability of modeling “odd type” data, including binary data, count data, and bounded clinical endpoint (e.g., ADAS-cog has a range of 0 to 70).
• Parallel computing ODE solving via the openmp package -- an industry's first among the current population PK/PD simulators to the best of our knowledge.
• An intuitive, powerful, graphic user interface (GUI) based project manager in shinyMixR.

v1.0.0-1

First major release

A comprehensive collection of non-linear mixed effect (nlme) model algorithms:

• First Order Conditional Estimate (FOCE)

## eg
nlmixr(modelfn, data, "focei", control=foceiControl(interaction=FALSE));

• First Order Conditional Estimate with interaction (FOCEi),

## eg
nlmixr(modelfn, data, "focei");

• Adaptive Gaussian quadrature (with Laplacian approximation as a special case see glmm and glmm2),
• Stochastic Approximation Estimation-Maximization (SAEM).

## eg
nlmixr(modelfn, data, "saem")

• Traditional R nlme

## eg
nlmixr(modelfn, data, "nlme")

Other features

• A minimalist, intuitive, expressive, and domain-specific nlme modeling language.
• The capability of joint modeling of multiple endpoints.
• A revamped SAEM engine with improved speed and stability.
• The capability of out-of-box Visual Predictive Checks (VPC) after a model fit.
• The capability of out-of-box sophisticated Clinical Trial Simulation (CTS) after a model fit.
• The capability of an out-of-box comprehensive diagnostic kit with a direct hook to xpose after a model fit.
• The capability of modeling “odd type” data, including binary data, count data, and bounded clinical endpoint (e.g., ADAS-cog has a range of 0 to 70).
• Parallel computing ODE solving via the openmp package -- an industry's first among the current population PK/PD simulators to the best of our knowledge.
• An intuitive, powerful, graphic user interface (GUI) based project manager in shinyMixR.

v0.9.1-3

CRAN release:

• No longer depend on RxODE or nlme, rather import them instead.