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'.

Fast exponentiation when the exponent is an integer.

Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.

Full pattern summation of X-ray powder diffraction data as
  described in Chipera and Bish (2002) <doi:10.1107/S0021889802017405> and
  Butler and Hillier (2021) <doi:10.1016/j.cageo.2020.104662>.
  Derives quantitative estimates of crystalline and amorphous phase
  concentrations in complex mixtures.

Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations
    allow for transforming variables with distributions distant from 0 to
    normality. Transformers are implemented as S4 objects. These allow for
    transforming new instances to normality after optimising fitting parameters
    on other data. A test for central normality allows for rejecting 
    transformations that fail to produce a suitably normal distribution, 
    independent of sample number.

Contains functions to obtain the operational characteristics of 
    bioequivalence studies in Two-Stage Designs (TSD) via simulations.

The first goal of this package is to provide a multitude of tree models,
    i.e., functions that generate rooted binary trees with a given number of leaves.
    Second, the package allows for an easy evaluation and comparison of tree shape
    statistics by estimating their power to differentiate between different tree models.
    Please note that this R package was developed alongside the manuscript
    "Tree balance in phylogenetic models" by
    S. J. Kersting, K. Wicke, and M. Fischer (2024) <doi:10.48550/arXiv.2406.05185>,
    which provides further background and the respective mathematical definitions.
    This project was supported by the project ArtIGROW, which is a part of the
    WIR!-Alliance ArtIFARM –  Artificial Intelligence in Farming funded by the
    German Federal Ministry of Education and Research (No. 03WIR4805).