Computing Envelope Estimators
Bootstrap for env.apweights
Bootstrap for env
Bootstrap for env.tcond
Bootstrap for eppls
Bootstrap for genv
Bootstrap for henv
Bootstrap for logit.env
Bootstrap for penv
Bootstrap for pois.env
Bootstrap for rrenv.apweights
Bootstrap for rrenv
Bootstrap for senv
Bootstrap for stenv
Bootstrap for sxenv
Bootstrap for xenv
Cross validation for env.apweights
Cross validation for env
Cross validation for env.tcond
Cross validation for peplos
Cross validation for genv
Cross validation for henv
Cross validation for logit.env
Cross validation for penv
Cross validation for pois.env
Cross validation for rrenv.apweights
Cross validation for rrenv
Cross validation for senv
Cross validation for stenv
Cross validation for sxenv
Cross validation for xenv
Select the rank of beta
Fit the envelope model with nonconstant variance
Fit the response envelope model
Fit the envelope model with t-distributed errors
Fit the Envelope-based Partial Partial Least Squares model
Fit the functional envelope linear model
Fit the functional envelope linear model
Fit the groupwise envelope model
Fit the heteroscedastic envelope model
Fit the envelope model in logistic regression
Fit the partial envelope model
Fit the envelope model in poisson regression
Estimation or prediction for env.apweights
Estimation or prediction for env
Estimation or prediction for env.tcond
Estimation or prediction for eppls
Estimation or prediction for felmdir
Estimation or prediction for felmKL
Estimation or prediction for genv
Estimation or prediction for henv
Estimation or prediction for logit.env
Estimation or prediction for penv
Estimation or prediction for pois.env
Estimation or prediction for rrenv.apweights
Estimation or prediction for rrenv
Estimation or prediction for senv
Estimation or prediction for stenv
Estimation or prediction for sxenv
Estimation or prediction for xenv
Estimation or prediction for env
Computing Envelope Estimators
Fit the reduced-rank envelope model with nonconstant variance
Fit the reduced-rank envelope model
Fit the scaled response envelope model
Fit the simultaneous envelope model
Fit the scaled predictor envelope model
Hypothesis test of the coefficients of the response envelope model wit...
Hypothesis test of the coefficients of the response envelope model
Hypothesis test of the coefficients of the response envelope model wit...
Hypothesis test of the coefficients of the groupwise envelope model
Hypothesis test of the coefficients of the heteroscedastic envelope mo...
Hypothesis test of the coefficients of the envelope model
Hypothesis test of the coefficients of the partial envelope model
Hypothesis test of the coefficients of the envelope model
Hypothesis test of the coefficients of the reduced rank envelope model...
Hypothesis test of the coefficients of the reduced rank envelope model
Hypothesis test of the coefficients of the scaled response envelope mo...
Hypothesis test of the coefficients of the simultaneous envelope model
Hypothesis test of the coefficients of the scaled predictor envelope m...
Hypothesis test of the coefficients of the predictor envelope model
Select the dimension of env.apweights
Select the dimension of env
Select the dimension of env.tcond
Select the dimension of eppls
Find the envelope dimensions in the functional envelope linear model
Find the envelope dimensions in the functional envelope linear model
Select the dimension of genv
Select the dimension of henv
Select the dimension of logit.env
Select the dimension of penv
Select the dimension of pois.env
Select the dimension of the constructed partial envelope for predictio...
Select the dimension of rrenv.apweights
Select the dimension of rrenv
Select the dimension of senv
Select the dimension of stenv
Select the dimension of sxenv
Select the dimension of xenv
Weighted response envelope estimator
Weighted partial envelope estimator
Estimation or prediction using weighted partial envelope
Weighted predictor envelope estimator
Fit the predictor envelope model
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.