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rchemo  

Dimension Reduction, Regression and Discrimination for Chemometrics
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("rchemo", "0.1-3")



Attach the package and use:
library("rchemo")
Maintained by
Marion Brandolini-Bunlon
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-05-12
Latest Update: 2023-05-12
Description:
Data exploration and prediction with focus on high dimensional data and chemometrics. The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data. The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains. Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()).
How to cite:
Marion Brandolini-Bunlon (2023). rchemo: Dimension Reduction, Regression and Discrimination for Chemometrics. R package version 0.1-3, https://cran.r-project.org/web/packages/rchemo. Accessed 18 Feb. 2025.
Previous versions and publish date:
0.1-1 (2023-05-12 16:00), 0.1-2 (2024-06-05 20:30)
Other packages that cited rchemo R package
View rchemo citation profile
Other R packages that rchemo depends, imports, suggests or enhances
Complete documentation for rchemo
Functions, R codes and Examples using the rchemo R package
Some associated functions: aggmean . aicplsr . asdgap . blocks . cassav . cglsr . checkdupl . checkna . covsel . dderiv . detrend . dfplsr_cg . dkplsr . dkrr . dmnorm . dtagg . dummy . epo . euclsq . fda . forages . getknn . gram . gridcv . gridscore . headm . interpl . knnda . knnr . kpca . kplsr . kplsrda . krr . krrda . lda . lmr . lmrda . locw . lwplsda_agg . lwplsr . lwplsr_agg . lwplsrda . matW . mavg . octane . odis . ozone . pca . pinv . plotjit . plotscore . plotsp . plotxna . plotxy . plsda . plsda_agg . plsr . plsr_agg . rmgap . rr . rrda . sampcla . sampdp . sampks . savgol . scordis . scores . segmkf . selwold . snv . sourcedir . summ . svm . transform . wdist . xfit . 
Some associated R codes: aggmean.R . aicplsr.R . blocks.R . cglsr.R . checking.R . covsel.R . dfplsr_cov.R . dfplsr_div.R . distances.R . dkplsr.R . dkrr.R . dmnorm.R . dtagg.R . dummy.R . eposvd.R . fda.R . getknn.R . gridcv.R . gridscore.R . headm.R . kernels.R . knnda.R . knnr.R . kpca.R . kplsr.R . kplsrda.R . krr.R . krrda.R . lda.R . lmr.R . lmrda.R . locw.R . lwplsprobda.R . lwplsprobda_agg.R . lwplsr.R . lwplsr_agg.R . lwplsrda.R . lwplsrda_agg.R . matW.R . mpars.R . odis.R . pcaeigen.R . pcaeigenk.R . pcanipals.R . pcanipalsna.R . pcasph.R . pcasvd.R . pinv.R . plotjit.R . plotscore.R . plotsp.R . plotsp1.R . plotxna.R . plotxy.R . plsda_agg.R . plskern.R . plsnipals.R . plsprobda.R . plsr_agg.R . plsrannar.R . plsrda.R . preprocessing.R . qda.R . rmgap.R . rr.R . rrda.R . sampcla.R . sampdp.R . sampks.R . scordis.R . segmkf.R . segmts.R . selwold.R . summ.R . svm.R . wdist.R . xfit.R . zfunctions.R .  Full rchemo package functions and examples
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