c(person("Venelin", "Mitov", email = "vmitov@gmail.com",
role = c("aut", "cre", "cph")),
person("Matt", "Dowle",
role = c("ctb")))
The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to
estimate the phylogenetic heritability of continuous traits, to test
hypotheses of neutral evolution versus stabilizing selection, to quantify
the strength of stabilizing selection, to estimate measurement error and to
make predictions about the evolution of a phenotype and phenotypic variation
in a population. The package implements combined maximum likelihood and
Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed
Model, fast parallel likelihood calculation, maximum likelihood
inference of the genotypic values at the tips, functions for summarizing and
plotting traces and posterior samples, functions for simulation of a univariate
continuous trait evolution model along a phylogenetic tree. So far, the
package has been used for estimating the heritability of quantitative traits
in macroevolutionary and epidemiological studies, see e.g.
Bertels et al. (2017) <doi:10.1093/molbev/msx246> and
Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for
parallel POUMM likelihood calculation has been published in
Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.
testthat, usethis, Rmpfr, mvtnorm, lmtest, knitr, rmarkdown,
parallel, doParallel
Venelin Mitov [aut, cre, cph],
Matt Dowle [ctb]

c(person("Venelin", "Mitov", email = "vmitov@gmail.com",
comment = '<a href="https://venelin.github.io">venelin.github.io</a>',
role = c("aut", "cre", "cph")),
person("Matt", "Dowle",
role = c("ctb")))
Venelin Mitov [aut, cre, cph] (<a
href="https://venelin.github.io">venelin.github.io</a>),
Matt Dowle [ctb]

ape, data.table(>= 1.13.2), coda, foreach, ggplot2, lamW,
adaptMCMC, utils

Partition of Variation Variance Component Analysis Method
An implementation of the Partition Of variation (POV) method as
developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in
1993 for the analysis of semiconductor data for hard drive manufacturing.
POV is based on sequential sum of squares and is an exact method that
explains all observed variation. It quantitates both the between and within
factor variation effects and can quantitate the influence of both continuous
and categorical factors.
person("Paul", "Deen", email = "paulext@gmail.com",
role = c("aut", "cre"))