Computing Envelope Estimators // Provides a general routine,
envMU, which allows estimation of the M envelope of span(U)
given root n consistent estimators of M and U. The routine
envMU does not presume a model. This package implements
response envelopes, partial response envelopes, envelopes in
the predictor space, heteroscedastic envelopes, simultaneous
envelopes, scaled response envelopes, scaled envelopes in the
predictor space, groupwise envelopes, weighted envelopes,
envelopes in logistic regression, envelopes in Poisson
regression envelopes in function-on-function linear regression,
envelope-based Partial Partial Least Squares, envelopes with
non-constant error covariance, envelopes with t-distributed
errors, reduced rank envelopes and reduced rank envelopes with
non-constant error covariance. For each of these model-based
routines the package provides inference tools including
bootstrap, cross validation, estimation and prediction,
hypothesis testing on coefficients are included except for
weighted envelopes. Tools for selection of dimension include
AIC, BIC and likelihood ratio testing. Background is
available at Cook, R. D., Forzani, L. and Su, Z. (2016)
doi:10.1016/j.jmva.2016.05.006. Optimization is based on a
clockwise coordinate descent algorithm.