Calculations of "EP15-A3 document. A manual for user verification of precision and estimation of bias" CLSI (2014, ISBN:1-56238-966-1).

Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.

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() and lm_lin() 
    (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'), and rma.uni() 
    and rma.mv() (from 'metafor').

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>.