Implements a classification method described by Grice (2011, ISBN:978-0-12-385194-9) using
    binary procrustes rotation; a simplified version of procrustes rotation.

Provides several cluster-robust variance estimators (i.e.,
    sandwich estimators) for ordinary and weighted least squares linear regression
    models, including the bias-reduced linearization estimator introduced by Bell
    and McCaffrey (2002) 
    <https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf> and 
    developed further by Pustejovsky and Tipton (2017) 
    <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating
    the variance- covariance matrix and for testing single- and multiple-
    contrast hypotheses based on Wald test statistics. Tests of single regression
    coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-
    contrast hypotheses use an approximation to Hotelling's T-squared distribution.
    Methods are provided for a variety of fitted models, including lm() and mlm
    objects; glm(); geeglm() (from package 'geepack'); lm_robust(), lm_lin(), and 
    iv_robust() (from package 'estimatr'); ivreg() (from package 'AER'); ivreg() 
    (from package 'ivreg' when estimated by ordinary least squares); plm() (from 
    package 'plm'); gls() and lme() (from 'nlme'); lmer() (from `lme4`); robu() (from 
    'robumeta'); rma.uni() and rma.mv() (from 'metafor'); and mmrm() (from 'mmrm').

CLUster Evaluation (CLUE) is a computational method for identifying 
              optimal number of clusters in a given time-course dataset clustered by 
              cmeans or kmeans algorithms and subsequently identify key kinases or 
              pathways from each cluster. Its implementation in R is called ClueR. 
              See README on <https://github.com/PYangLab/ClueR> for more details.
              P Yang et al. (2015) <doi:10.1371/journal.pcbi.1004403>.

An implementation of the clugen algorithm for generating
    multidimensional clusters with arbitrary distributions. Each cluster is
    supported by a line segment, the position, orientation and length of which
    guide where the respective points are placed. This package is described in
    Fachada & de Andrade (2023) <doi:10.1016/j.knosys.2023.110836>.