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mvdalab  

Multivariate Data Analysis Laboratory
View on CRAN: Click here


Download and install mvdalab package within the R console
Install from CRAN:
install.packages("mvdalab")

Install from Github:
library("remotes")
install_github("cran/mvdalab")

Install by package version:
library("remotes")
install_version("mvdalab", "1.7")



Attach the package and use:
library("mvdalab")
Maintained by
Nelson Lee Afanador
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-04-09
Latest Update: 2022-10-05
Description:
An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
How to cite:
Nelson Lee Afanador (2016). mvdalab: Multivariate Data Analysis Laboratory. R package version 1.7, https://cran.r-project.org/web/packages/mvdalab. Accessed 30 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:33), 1.0 (2016-04-09 00:40), 1.1 (2016-07-09 10:45), 1.2 (2017-03-01 08:36), 1.3 (2017-10-04 16:38), 1.4 (2017-10-16 17:10), 1.5 (2021-05-15 01:30), 1.6 (2021-07-14 19:30)
Other packages that cited mvdalab R package
View mvdalab citation profile
Other R packages that mvdalab depends, imports, suggests or enhances
Complete documentation for mvdalab
Functions, R codes and Examples using the mvdalab R package
Some associated functions: BiPlot . College . MVComp . MVcis . MultCapability . PE . Penta . R2s . ScoreContrib . SeqimputeEM . T2 . Wang_Chen . Wang_Chen_Sim . Xresids . XresidualContrib . acfplot . ap.plot . bca.cis . bidiagpls.fit . boot.plots . coef.mvdareg . coefficients.boots . coefficients . coefficientsplot2D . coefsplot . contr.niets . ellipse.mvdalab . imputeBasic . imputeEM . imputeQs . imputeRough . introNAs . jk.after.boot . loadings.boots . loadings . loadingsplot . loadingsplot2D . mewma . model.matrix.mvdalab . mvdaboot . mvdalab-package-title . mvdaloo . mvrnorm.svd . my.dummy.df . no.intercept . pca.nipals . pcaFit . perc.cis . plot.R2s . plot.cp . plot.mvcomp . plot.mvdareg . plot.plusminus . plot.smc . plot.sr . plot.wrtpls . plsFit . plusMinusDat . plusminus.fit . plusminus.loo . plusminusFit . predict.mvdareg . print.mvdalab . print.plusminus . proCrustes . scoresplot . smc.acfTest . smc . sr . weight.boots . weights . weightsplot . weightsplot2D . wrtpls.fit . y.loadings.boots . y.loadings . 
Some associated R codes: BiPlot.R . MVComp.R . MVcis.R . MultCapability.R . PE.R . R2s.R . ScoreContrib.R . SeqimputeEM.R . T2.R . Xresids.R . XresidualContrib.R . acfplot.R . ap.plot.R . bca.cis.R . bidiagpls.fit.R . boot.plots.R . coef.mvdareg.R . coefficients.boots.R . coefficients.mvdareg.R . coefficientsplot2D.R . coefsplot.R . contr.niets.R . ellipse.mvdalab.R . imputeBasic.R . imputeEM.R . imputeQs.R . imputeRough.R . introNAs.R . jk.after.boot.R . loadings.boots.R . loadings.mvdareg.R . loadingsplot.R . loadingsplot2D.R . mewma.R . model.matrix.mvdareg.R . mvdaboot.R . mvdalab.R . mvdaloo.R . mvrnorm.svd.R . mvrnormBase.svd.R . my.dummy.df.R . no.intercept.R . pca.nipals.R . pcaFit.R . perc.cis.R . plot.R2s.R . plot.cp.R . plot.mvcomp.R . plot.mvdapca.R . plot.mvdareg.R . plot.plusminus.R . plot.wrtpls.R . plsFit.R . plusminus.fit.R . plusminus.loo.R . plusminusFit.R . predict.mvdareg.R . print.empca.R . print.mvdapca.R . print.mvdareg.R . print.npca.R . print.plusminus.R . print.proC.R . print.seqem.R . proCrustes.R . scoresplot.R . smc.R . smc.acfTest.R . sr.R . summary.mvdareg.R . summary.plusminus.R . weight.boots.R . weights.mvdareg.R . weightsplot.R . weightsplot2D.R . wrtpls.fit.R . y.loadings.R . y.loadings.boots.R .  Full mvdalab package functions and examples
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