Provides convenience functions and pre-programmed Stan models
related to the paired comparison factor model. Its purpose is to make fitting
paired comparison data using Stan easy.
rstan (>= 2.18), reshape2, mvtnorm, igraph, loo

Stan Models for the Paired Comparison Factor Model

c(person(given = c("Joshua", "N."),
family = "Pritikin",
role = c("aut", "cre"),
email = "jpritikin@pobox.com",
comment = c(ORCID = "0000-0002-9862-5484")),
person(given = "Daniel C.",
family = "Furr",
role = "ctb",
email = "danielcfurr@berkeley.edu"),
person(given = "Trustees of Columbia University",
role = "cph"))
Provides convenience functions and pre-programmed
Stan models related to the paired comparison factor model. Its purpose
is to make fitting paired comparison data using Stan easy.
rstan (>= 2.18), reshape2, mvtnorm, igraph, loo, lifecycle
knitr, rmarkdown, testthat, shiny, ggplot2, covr, qgraph,
Matrix

Provides convenience functions and pre-programmed
Stan models related to the paired comparison factor model. Its purpose
is to make fitting paired comparison data using Stan easy. This
package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.

rstan (>= 2.18), rstantools (>= 2.1.1), reshape2, mvtnorm,
igraph, loo, lifecycle

IRT models for polytomous and continuous item responses
pcIRT estimates the multidimensional polytomous Rasch model (Rasch, 1961) and the Continous Rating Scale model (Mueller, 1987)

IRT Models for Polytomous and Continuous Item Responses
Rcpp, combinat, stats, graphics, grDevices, utils, methods
Estimates the multidimensional polytomous Rasch model
(Rasch, 1961) and the Continuous Rating Scale model (Mueller, 1987).

Estimates the multidimensional polytomous Rasch model (Rasch, 1961) with conditional maximum likelihood estimation.

pcIRT estimates the multidimensional polytomous Rasch model
(Rasch, 1961) and the Continous Rating Scale model (Mueller, 1987).

Jerome Friedman, Kenneth Tay, Robert Tibshirani
A method for fitting the entire regularization path
of the principal components lasso for linear and
logistic regression models. The algorithm uses cyclic coordinate descent
in a path-wise fashion. See URL below for more information on the algorithm.
See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression'
<arXiv:1810.04651>.