Provides high-level modeling functions to define and train
models using the 'torch' R package. Models include linear, logistic,
and multinomial regression as well as multilayer perceptrons.
covr, modeldata, purrr, recipes, spelling, testthat,
yardstick

person("Toshiaki", "Ara", email = "toshiaki.ara@gmail.com",
role = c("aut", "cre"))
Provides the functions for Brunner-Munzel test and
permuted Brunner-Munzel test,
which enable to use formula, matrix, and table as argument.
These functions are based on Brunner and Munzel (2000)
<doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>
and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>.

Provides the functions for Brunner-Munzel test and
permuted Brunner-Munzel test,
which enable to use formula, matrix, and table as argument.
These functions are based on Brunner and Munzel (2000)
<doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>
and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>,
and are written with FORTRAN.

When samples contain missing data, are small, or are suspected of bias,
estimation of scale reliability may not be trustworthy.  A recommended solution
for this common problem has been Bayesian model estimation.  Bayesian methods
rely on user specified information from historical data or researcher intuition
to more accurately estimate the parameters.  This package provides a user friendly
interface for estimating test reliability.  Here, reliability is modeled as a beta
distributed random variable with shape parameters alpha=true score variance and
beta=error variance (Tanzer, 2020) <https://smep.org/>.

When samples contain missing data, are small, or are suspected of bias,
estimation of scale reliability may not be trustworthy.  A recommended solution
for this common problem has been Bayesian model estimation.  Bayesian methods
rely on user specified information from historical data or researcher intuition
to more accurately estimate the parameters.  This package provides a user friendly
interface for estimating test reliability.  Here, reliability is modeled as a beta
distributed random variable with shape parameters alpha=true score variance and
beta=error variance (Tanzer & Harlow, 2020) <doi:10.1080/00273171.2020.1854082>.