Functions to compute pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.

Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. 
	Researchers often work with data taking the form of proportions that can be modeled with 
	a beta distribution. Here we present an R package, 'BetaPASS', that perform power and 
	sample size calculations for data following a beta distribution with comparative 
	nonparametric output. This package allows flexibility with multiple options for link 
	functions to fit the data and graphing functionality for visual comparisons.

Permutational method to incorporate taxonomic uncertainty  and some functions to assess its effects on parameters of some widely used multivariate methods in ecology, as explained in Cayuela et al. (2011) <doi:10.1111/j.1600-0587.2009.05899.x>.

Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1),
  e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>.
  Moreover, extended-support beta regression models can accommodate dependent variables with
  boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2025) <doi:10.1093/jrsssc/qlaf039>.
  For the classical beta regression model, alternative specifications are provided:
  Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for
  beta regression, see Grün, Kosmidis, and Zeileis (2012) <doi:10.18637/jss.v048.i11>.

Generates robust confidence intervals for standardized regression coefficients
    using heteroskedasticity-consistent standard errors for models fitted by lm()
    as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>.
    The package can also be used to generate confidence intervals for R-squared,
    adjusted R-squared, and differences of standardized regression coefficients.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.