Packaged: Mon Apr  3 13:42:36 2006; socher

Depends: R (>= 3.0), MASS, boot, parallel
Description: Routines for PLS-based genomic analyses,
implementing PLS methods for classification with
data: GSIM and Ridge PLS. The >=1.3 versions includes a
new classification method combining variable selection and
compression in logistic regression context: RIRLS-SPLS; and

Description: Routines for PLS-based genomic analyses.
They implements PLS methods for classification with
data: GSIM and Ridge PLS. The >=1.3 versions includes an other

Imports: MASS, boot, parallel, reshape2, plyr, fields, RhpcBLASctl
compression in logistic regression context: logit-SPLS; and

Title: Model Wrappers for Projection Methods
Description: Bindings for additional regression models for use
with the 'parsnip' package, including ordinary and spare partial least
squares models for regression and classification (Rohart et al (2017)
Imports: tibble, mixOmics, dplyr, tidyr, generics, magrittr, purrr,
Suggests: modeldata, testthat, covr, spelling

Description: Bindings for additional regression models for use with the
'parsnip' package, including ordinary and spare partial least squares
models for regression and classification (Rohart et al (2017)
Imports: dplyr, generics, magrittr, mixOmics, purrr, rlang, tibble,
Suggests: covr, modeldata, spelling, testthat

Suggests: rmarkdown, covr, modeldata, spelling, testthat (>= 3.0.0)

Title: Linear and Smooth Predictor Modelling with Penalisation and
Authors@R: c(person("Indrayudh", "Ghosal", email = "ig248@cornell.edu", role = c("aut", "cre")),
person("Matthias", "Kormaksson", email = "matthias.kormaksson@novartis.com", role = "aut"))
Description: Fit a model with potentially many linear and smooth predictors. Interaction
effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties
we use iterative steps alternating between using linear predictors (lasso) and smooth predictors
Suggests: tidyverse (>= 1.2.1), knitr, rmarkdown, kableExtra
Packaged: 2019-07-16 19:05:56 UTC; Indrayudh