Description: Load the Just Another Gibbs Sampling (JAGS) module 'pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.

Title: Phylogenetics for the Environmental Sciences
Author: William D. Pearse, Marc W. Cadotte, Jeannine Cavender-Bares,
Caroline Tucker, Steve C. Walker, Matthew R. Helmus
Maintainer: William D. Pearse <will.pearse@gmail.com>
Description: Eco-phylogenetic and community phylogenetic analyses.
Keeps community ecological and phylogenetic data matched up and
comparable using 'comparative.comm' objects. Wrappers for common
community phylogenetic indices ('shape', 'evenness', 'dispersion',
and 'dissimilarity' metrics). Implementation of Cavender-Bares
(2004) correlation of phylogenetic and ecological matrices
('fingerprint.regression'). Simulation of null assemblages,
traits, and phylogenies ('scape', 'sim.meta.comm').

Anthony R. Ives, Caroline Tucker, Steve C. Walker, Matthew R. Helmus
('fingerprint.regression'). Phylogenetic Generalised Linear Mixed
Models (PGLMMs) following ('pglmm') following Ives & Helmus (2011)
and Rafferty & Ives (2013). Simulation of null assemblages,

community phylogenetic indices ('pez.shape', 'pez.evenness',
'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation
of Cavender-Bares (2004) correlation of phylogenetic and
Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives &
Helmus (2011) and Rafferty & Ives (2013). Simulation of null
assemblages, traits, and phylogenies ('scape', 'sim.meta.comm').

Title: Estimates False Discovery Proportion Under Arbitrary Covariance
Author: Jianqing Fan, Tracy Ke, Sydney Li and Lucy Xia
Description: This package contains functions for performing multiple
testing and estimating the false discovery proportion (FDP).

Description: Estimate the false discovery proportion (FDP) by Principal Factor Approximation method with general known and unknown covariance dependence.