Maximum Likelihood estimation of random utility discrete
choice models, as described in Kenneth Train (2009) Discrete Choice
Methods with Simulations <doi:10.1017/CBO9780511805271>.

Formula, zoo, lmtest, statmod, MASS, Rdpack

Maximum likelihood estimation of random utility discrete
choice models. The software is described in Croissant (2020)
<doi:10.18637/jss.v095.i11> and the underlying methods in
Train (2009) <doi:10.1017/CBO9780511805271>.

Bayesian Model Averaging for Multinomial Logit Models
Provides a modified function bic.glm of the BMA package
that can be applied to multinomial logit (MNL) data. The data
is converted to binary logit using the Begg & Gray
approximation. The package also contains functions for maximum
likelihood estimation of MNL.

Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.

'Rcpp' Integration for the 'mlpack' Library
A fast, flexible machine learning library, written in C++, that
aims to provide fast, extensible implementations of cutting-edge
machine learning algorithms.  See also Curtin et al. (2018)
<doi:10.21105/joss.00726>.

A C++14 compiler. Version 5 or later of GCC will be
fine.
Rcpp, RcppArmadillo (>= 0.9.800), RcppEnsmallen (>=
0.2.10.0)

A Power Analysis Toolbox to Find Cost-Efficient Study Designs
We implement a surrogate modeling algorithm to guide simulation-based
sample size planning. The method is described in detail in a recent preprint
(Zimmer & Debelak (2022) <doi:10.31234/osf.io/tnhb2>).
It supports multiple study design parameters and optimization with respect to a
cost function. It can find optimal designs that correspond to a desired
statistical power or that fulfill a cost constraint.
utils, stats, DiceKriging, digest, ggplot2, randtoolbox,
rlist, WeightSVM, rgenoud
knitr, lme4, lmerTest, mirt, pwr, rmarkdown, simr, sn, tidyr