Author: Venelin Mitov [aut, cre, cph] (<a

models. The PCMFit package provides functionality for inference of

Imports: ape, expm, mvtnorm, data.table, ggplot2
Suggests: testthat, knitr, rmarkdown, abind, ggtree, covr

Suggests: testthat, knitr, rmarkdown, abind, ggtree, cowplot, covr

Title: Fast Likelihood Calculation for Phylogenetic Comparative Models
Description: Provides a C++ backend for multivariate phylogenetic comparative
models implemented in the R-package 'PCMBase'. Can be used in combination
with 'PCMBase' to enable fast and parallel likelihood calculation. Implements
the pruning likelihood calculation algorithm described in Mitov et al. (2018)
<arXiv:1809.09014>. Uses the 'SPLITT' C++ library for parallel tree traversal

Title: Model Response Styles in Partial Credit Models
Description: Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2016) <https://epub.ub.uni-muenchen.de/29373/> .  PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models.
Depends: ltm, statmod, cubature, mvtnorm, parallel
Packaged: 2017-11-06 13:44:23 UTC; schaubergerg

Packaged: 2017-11-15 08:08:30 UTC; schaubergerg

Packaged: 2018-01-04 09:52:19 UTC; schaubergerg

Description: Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) <doi:10.1177/0146621617748322> .  PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models.

Title: Bandwidth Selector with Penalized Comparison to Overfitting
Description: Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.)
criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019)
It can be used with univariate and multivariate data.