Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>,
Ghislain Durif <gd.dev@libertymail.net>,
Sophie Lambert-Lacroix
<sophie.lambert-lacroix@univ-grenoble-alpes.fr>, Julie Peyre
<Julie.Peyre@univ-grenoble-alpes.fr>, and Korbinian Strimmer
<k.strimmer@imperial.ac.uk>.
MASS, boot, parallel, reshape2, plyr, fields, RhpcBLASctl
Routines for PLS-based genomic analyses,
implementing PLS methods for classification with
microarray data and prediction of transcription factor
activities from combined ChIP-chip analysis. The >=1.2-1
versions include two new classification methods for microarray
data: GSIM and Ridge PLS. The >=1.3 versions includes a
new classification method combining variable selection and
compression in logistic regression context: logit-SPLS; and
an adaptive version of the sparse PLS.

c(person(given = "Max",
family = "Kuhn",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-2402-136X"),
email = "max@rstudio.com"),
person(given = "RStudio",
role = "cph"))
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)
<doi:10.1371/journal.pcbi.1005752>).
tibble, mixOmics, dplyr, tidyr, generics, magrittr, purrr,
rlang

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)
<doi:10.1371/journal.pcbi.1005752>).
dplyr, generics, magrittr, mixOmics, purrr, rlang, tibble,
tidyr

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

Linear and Smooth Predictor Modelling with Penalisation and
Variable Selection
c(person("Indrayudh", "Ghosal", email = "ig248@cornell.edu", role = c("aut", "cre")),
person("Matthias", "Kormaksson", email = "matthias.kormaksson@novartis.com", role = "aut"))
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
(generalised additive model).
tidyverse (>= 1.2.1), knitr, rmarkdown, kableExtra
Indrayudh Ghosal [aut, cre],
Matthias Kormaksson [aut]