Packaged: 2018-10-19 08:41:21 UTC; NikPardaz

Author: Mahdi Teimouri and Saralees Nadarajah
Description: Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.
Packaged: 2019-10-04 19:36:11 UTC; NikPardaz

Title: Modeling Phylogenetic Signals using Eigenvector Maps
Author: Guillaume Guenard, with contribution from Pierre Legendre
Description: Computational tools to represent phylogenetic signals using adapted eigenvector maps.

Title: Analyze Multinomial Processing Tree Models
Author: Henrik Singmann and David Kellen
Description: The package provides a user-friendly way for analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for individual and multi-individual data. The main functions perform model fitting and model selection. Model selection can be done using e.g. the minimum description length measure based on the Fischer information approximation (FIA). The FIA can be obtained for any MPT model using the algorithm provided by Wu, Myung, & Batchelder (2010, JMP) ported to R. The model and restrictions can easily be specified in external files. The 'classical' .EQN file format for model files is also supported. Furthermore, MPTinR supports multicore fitting using the snowfall package.