Package: packMBPLSDA 0.9.0

Marion Brandolini-Bunlon

packMBPLSDA: Multi-Block Partial Least Squares Discriminant Analysis

Several functions are provided to implement a MBPLSDA : components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. The use of this package is described in Brandolini-Bunlon et al (2019. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134).

Authors:Marion Brandolini-Bunlon, Stephanie Bougeard, Melanie Petera, Estelle Pujos-Guillot

packMBPLSDA_0.9.0.tar.gz
packMBPLSDA_0.9.0.tar.gz(r-4.5-noble)packMBPLSDA_0.9.0.tar.gz(r-4.4-noble)
packMBPLSDA_0.9.0.tgz(r-4.4-emscripten)packMBPLSDA_0.9.0.tgz(r-4.3-emscripten)
packMBPLSDA.pdf |packMBPLSDA.html✨
packMBPLSDA/json (API)

# Install 'packMBPLSDA' in R:

install.packages('packMBPLSDA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

medical - Medical dataset
nutrition - Nutritional dataset
omics - Metabolomic dataset
status - Physiopathological status data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 3 scripts 144 downloads 12 exports 109 dependencies

Last updated 3 years agofrom:a236b18f67. Checks:OK: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 08 2024
R-4.5-linux	OK	Dec 08 2024

Exports:boot_mbplsda cvpred_mbplsda disjunctive mbplsda permut_mbplsda plot_boot_mbplsda plot_cvpred_mbplsda plot_permut_mbplsda plot_pred_mbplsda plot_testdim_mbplsda pred_mbplsda testdim_mbplsda

Dependencies:abind ade4 backports base64enc boot broom bslib cachem car carData cli cluster codetools colorspace cowplot cpp11 crosstalk Deriv digest doBy doParallel dplyr DT ellipse emmeans estimability evaluate FactoMineR fansi farver fastmap flashClust fontawesome foreach Formula fs generics ggplot2 ggrepel glue gtable highr htmltools htmlwidgets httpuv isoband iterators jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle lme4 magrittr MASS Matrix MatrixModels memoise mgcv microbenchmark mime minqa modelr multcompView munsell mvtnorm nlme nloptr nnet numDeriv pbkrtest pillar pixmap pkgconfig plyr pROC promises purrr quantreg R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen rlang rmarkdown sass scales scatterplot3d sp SparseM stringi stringr survival tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml

Help page	Topics
Multi-Block Partial Least Squares Discriminant Analysis	packMBPLSDA-package packMBPLSDA
bootstraped simulations for multi-block partial least squares discriminant analysis	boot_mbplsda
Cross-validated predicted categories from a multi-block partial least squares discriminant model	cvpred_mbplsda
Disjunctive table	disjunctive
generalized inverse of a matrix X	ginv
inertia of a matrix	inertie
Multi-block partial least squares discriminant analysis	mbplsda
medical dataset	medical
nutritional dataset	nutrition
metabolomic dataset	omics
Permutation testing of a multi-block partial least squares discriminant model	permut_mbplsda
Plot the results of the fonction boot_mbplsda in a pdf file	plot_boot_mbplsda
Plot the results of the fonction cvpred_mbplsda in a pdf file	plot_cvpred_mbplsda
Plot the results of the fonction permut_mbplsda in a pdf file	plot_permut_mbplsda
Plot the results of the fonction pred_mbplsda in a pdf file	plot_pred_mbplsda
Plot the results of the fonction testdim_mbplsda in a pdf file	plot_testdim_mbplsda
Observed parameters and predicted categories from a multi-block partial least squares discriminant model	pred_mbplsda
physiopathological status data	status
Test of number of components by two-fold cross-validation for a multi-block partial least squares discriminant model	testdim_mbplsda

Package: packMBPLSDA 0.9.0

packMBPLSDA: Multi-Block Partial Least Squares Discriminant Analysis

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