Suggests: arm, bartMachine, biglasso, bigmemory, caret, class, dbarts,
devtools, e1071, earth, extraTrees, gam, gbm, genefilter,
ggplot2, glmnet, Hmisc, ipred, KernelKnn, kernlab, knitr,
lattice, LogicReg, MASS, mda, mlbench, nloptr, nnet, party,
polspline, prettydoc, quadprog, randomForest, ranger,
RhpcBLASctl, ROCR, rmarkdown, rpart, SIS, speedglm, spls,
stepPlr, sva, testthat, xgboost (>= 0.6)

devtools, e1071, earth, extraTrees, gam (>= 1.15), gbm,
genefilter, ggplot2, glmnet, ipred, KernelKnn, kernlab, knitr,
lattice, LogicReg, MASS, mlbench, nloptr, nnet, party,
RhpcBLASctl, ROCR, rmarkdown, rpart, SIS, speedglm, spls, sva,

Suggests: arm, bartMachine, biglasso, bigmemory, caret, class,

Authors@R: c(person("Eric", "Polley", email = "epolley@uchicago.edu", role = c("aut", "cre")), person("Erin", "LeDell", role = c("aut")), person("Chris", "Kennedy", role = c("aut")), person("Sam", "Lendle", role = c("ctb")), person("Mark", "van der Laan", role = c("aut", "ths")))
devtools, e1071, earth, extraTrees, gbm, genefilter, ggplot2,
glmnet, ipred, KernelKnn, kernlab, knitr, lattice, LogicReg,
MASS, mlbench, nloptr, nnet, party, polspline, prettydoc,
quadprog, randomForest, ranger, RhpcBLASctl, ROCR, rmarkdown,
rpart, SIS, speedglm, spls, sva, testthat, xgboost (>= 0.6)

Description: This package implements the super learner prediction
method and contains a library of prediction algorithms to be
Suggests: arm, BayesTree, caret, class, DSA, e1071, earth, gam, gbm,
genefilter, glmnet, Hmisc, ipred, LogicReg, MASS, mda, mlbench,
multicore, nnet, party, polspline, randomForest, rpart, SIS,

snow, spls, stepPlr, lattice, ggplot2, sva

MASS, mda, mlbench, nnet, parallel, party, polspline,

Title: Supervised Principal Component Analysis
Author: Gen Li <gl2521@cumc.columbia.edu>, Haocheng Ding <haochengding@ufl.edu>, Jiayi Ji <jj2876@caa.columbia.edu>
Description: Dimension reduction of complex data with supervision from auxiliary information. The package contains a series of methods for different data types (e.g., multi-view or multi-way data) including the supervised singular value decomposition (SupSVD), supervised sparse and functional principal component (SupSFPC), supervised integrated factor analysis (SIFA) and supervised PARAFAC/CANDECOMP factorization (SupCP). When auxiliary data are available and potentially affect the intrinsic structure of the data of interest, the methods will accurately recover the underlying low-rank structure by taking into account the supervision from the auxiliary data. For more details, see the paper by Gen Li, <DOI:10.1111/biom.12698>.
Imports: RSpectra, psych, fBasics, R.matlab, glmnet, MASS, matrixStats,
timeSeries, stats, matlabr, spls, pracma, matlab