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>.

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.

The R package 'povmap' supports small area estimation of means and 
  poverty headcount rates. It adds several new features to the 'emdi' package
  (see "The R Package emdi for Estimating and Mapping Regionally Disaggregated 
  Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>). 
  These include new options for incorporating survey weights, ex-post 
  benchmarking of estimates, two additional transformations, several new 
  convenient functions to assist with reporting results, and a wrapper function 
  to facilitate access from 'Stata'.