Generates Monte Carlo confidence intervals
    for standardized regression coefficients (beta) and other effect sizes,
    including multiple correlation, semipartial correlations,
    improvement in R-squared, squared partial correlations,
    and differences in standardized regression coefficients,
    for models fitted by lm().
    'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect
    (Pesigan and Cheung, 2024 <doi:10.3758/s13428-023-02114-4>)
    and the sampling covariance matrix of regression coefficients
    (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>)
    to generate confidence intervals effect sizes in regression.

Generates nonparametric bootstrap confidence intervals
    (Efron and Tibshirani, 1993: <doi:10.1201/9780429246593>)
    for standardized regression coefficients (beta) and other effect sizes,
    including multiple correlation, semipartial correlations,
    improvement in R-squared, squared partial correlations,
    and differences in standardized regression coefficients,
    for models fitted by lm().

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>.