Package: rchemo 0.1-3

Marion Brandolini-Bunlon

rchemo: Dimension Reduction, Regression and Discrimination for Chemometrics

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()).

Authors:Marion Brandolini-Bunlon [aut, cre], Benoit Jaillais [aut], Jean-Michel Roger [aut], Matthieu Lesnoff [aut]

rchemo_0.1-3.tar.gz
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rchemo.pdf |rchemo.html
rchemo/json (API)
NEWS

# Install 'rchemo' in R:
install.packages('rchemo', repos = c('https://chemhouse-group.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/chemhouse-group/rchemo/issues

Datasets:

On CRAN:

3.56 score 3 stars 12 scripts 276 downloads 197 exports 7 dependencies

Last updated 2 months agofrom:301e72f8ba. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winOKNov 10 2024
R-4.5-linuxOKNov 10 2024
R-4.4-winOKNov 10 2024
R-4.4-macOKNov 10 2024
R-4.3-winOKNov 10 2024
R-4.3-macOKNov 10 2024

Exports:aggmeanaicplsrbiasblockscalcglsrcheckduplchecknacoef.Cglsrcoef.Dkplscoef.Dkrrcoef.Kplsrcoef.Krrcoef.Lmrcoef.Mbplsrcoef.Plsrcoef.Rrcor2covseldderivdetrenddfplsr_cgdfplsr_covdfplsr_divdkplsrdkrrdmnormdtaggdummyeposvderreuclsqeuclsq_mufdafdasvdgetknngridcvgridcvlbgridcvlvgridscoregridscorelbgridscorelvhconcatheadminterplknndaknnrkpcakplsrkplsrdakpolkrbfkrrkrrdaktanhldalmrlmrdalocwlocwlvlwplsldalwplslda_agglwplsqdalwplsqda_agglwplsrlwplsr_agglwplsrdalwplsrda_aggmahsqmahsq_mumatBmatWmavgmblocksmbplsldambplsqdambplsrmbplsr_mbplsda_allstepsmbplsrdamparsmsemsepodisorthogpcaeigenpcaeigenkpcanipalspcanipalsnapcasphpcasvdpinvplotjitplotscoreplotspplotsp1plotxnaplotxyplskernplsldaplslda_aggplsnipalsplsqdaplsqda_aggplsr_aggplsr_plsda_allstepsplsrannarplsrdaplsrda_aggpredict.Cglsrpredict.Dkplsrpredict.Dkrrpredict.Dmnormpredict.Knndapredict.Knnrpredict.Kplsrpredict.Kplsrdapredict.Krrpredict.Krrdapredict.Ldapredict.Lmrpredict.Lmrdapredict.Lwplsprobdapredict.Lwplsprobda_aggpredict.Lwplsrpredict.Lwplsr_aggpredict.Lwplsrdapredict.Lwplsrda_aggpredict.Mbplsprobdapredict.Mbplsrpredict.Mbplsrdapredict.Plsda_aggpredict.Plsprobdapredict.Plsrpredict.Plsr_aggpredict.Plsrdapredict.Qdapredict.Rrpredict.Rrdapredict.Soplsprobdapredict.Soplsrpredict.Soplsrdapredict.Svmqdar2residclaresidregrmgaprmseprpdrpdrrrrrdasampclasampdpsampkssavgolscordissegmkfsegmtsselwoldsepsnvsoplsldasoplsldacvsoplsqdasoplsqdacvsoplsrsoplsr_soplsda_allstepssoplsrcvsoplsrdasoplsrdacvsourcedirsummsummary.Fdasummary.Kpcasummary.Mbplsrsummary.Pcasummary.Plsrsummary.Svmsvmdasvmrtransformtransform.Dkplstransform.Fdatransform.Kpcatransform.Kplsrtransform.Mbplsrtransform.Pcatransform.Plsrtransform.Soplsprobdatransform.Soplsrtransform.Soplsrdavipwdistxfitxfit.Pcaxfit.Plsrxresid

Dependencies:classdata.tablee1071FNNMASSproxysignal

Readme and manuals

Help Manual

Help pageTopics
Centers of classesaggmean
AIC and Cp for Univariate PLSR Modelsaicplsr
asdgapasdgap
Block autoscalingblockscal hconcat mblocks
cassavcassav
CG Least Squares Modelscglsr coef.Cglsr predict.Cglsr
Duplicated rows in datasetscheckdupl
Find and count NA values in a datasetcheckna
CovSelcovsel
Derivation by finite differencedderiv
Polynomial de-trend transformationdetrend
Degrees of freedom of Univariate PLSR Modelsdfplsr_cg dfplsr_cov dfplsr_div
Direct KPLSR Modelscoef.Dkpls dkplsr predict.Dkplsr transform.Dkpls
Direct KRR Modelscoef.Dkrr dkrr predict.Dkrr
Multivariate normal probability densitydmnorm predict.Dmnorm
Summary statistics of data subsetsdtagg
Table of dummy variablesdummy
External parameter orthogonalization (EPO)eposvd
Matrix of distanceseuclsq euclsq_mu mahsq mahsq_mu
Factorial discriminant analysisfda fdasvd summary.Fda transform.Fda
foragesforages
KNN selectiongetknn
Cross-validationgridcv gridcvlb gridcvlv
Tuning of predictive models on a validation datasetgridscore gridscorelb gridscorelv mpars
Display of the first part of a data setheadm
Resampling of spectra by interpolation methodsinterpl
KNN-DAknnda predict.Knnda
KNN-Rknnr predict.Knnr
KPCAkpca summary.Kpca transform.Kpca
KPLSR Modelscoef.Kplsr kplsr predict.Kplsr transform.Kplsr
KPLSR-DA modelskplsrda predict.Kplsrda
Kernel functionskpol krbf ktanh
KRR (LS-SVMR)coef.Krr krr predict.Krr
KRR-DA modelskrrda predict.Krrda
LDA and QDAlda predict.Lda predict.Qda qda
Linear regression modelscoef.Lmr lmr predict.Lmr
LMR-DA modelslmrda predict.Lmrda
Locally weighted modelslocw locwlv
KNN-LWPLSRlwplsr predict.Lwplsr
Aggregation of KNN-LWPLSR models with different numbers of LVslwplsr_agg predict.Lwplsr_agg
KNN-LWPLS-DA Modelslwplslda lwplsqda lwplsrda predict.Lwplsprobda predict.Lwplsrda
Aggregation of KNN-LWPLSDA models with different numbers of LVslwplslda_agg lwplsqda_agg lwplsrda_agg predict.Lwplsprobda_agg predict.Lwplsrda_agg
Between and within covariance matricesmatB matW
Smoothing by moving averagemavg
multi-block PLSR algorithmscoef.Mbplsr mbplsr predict.Mbplsr summary.Mbplsr transform.Mbplsr
MBPLSR or MBPLSDA analysis stepsmbplsr_mbplsda_allsteps
multi-block PLSDA modelsmbplslda mbplsqda mbplsrda predict.Mbplsprobda predict.Mbplsrda
Residuals and prediction error ratesbias cor2 err mse msep r2 residcla residreg rmsep rpd rpdr sep
octaneoctane
Orthogonal distances from a PCA or PLS score spacelodis odis
Orthogonalization of a matrix to another matrixorthog
ozoneozone
PCA algorithmspcaeigen pcaeigenk pcanipals pcanipalsna pcasph pcasvd summary.Pca transform.Pca
Moore-Penrose pseudo-inverse of a matrixpinv
Jittered plotplotjit
Plotting errors ratesplotscore
Plotting spectraplotsp plotsp1
Plotting Missing Data in a Matrixplotxna
2-d scatter plotplotxy
PLSR algorithmscoef.Plsr plskern plsnipals plsrannar predict.Plsr summary.Plsr transform.Plsr
PLSR with aggregation of latent variablesplsr_agg predict.Plsr_agg
PLSR or PLSDA analysis stepsplsr_plsda_allsteps
PLSDA modelsplslda plsqda plsrda predict.Plsprobda predict.Plsrda
PLSDA with aggregation of latent variablesplslda_agg plsqda_agg plsrda_agg predict.Plsda_agg
Removing vertical gaps in spectrarmgap
Linear Ridge Regressioncoef.Rr predict.Rr rr
RR-DA modelspredict.Rrda rrda
Within-class samplingsampcla
Duplex samplingsampdp
Kennard-Stone samplingsampks
Savitzky-Golay smoothingsavgol
Score distances (SD) in a PCA or PLS score spacescordis
Segments for cross-validationsegmkf segmts
Heuristic selection of the dimension of a latent variable model with the Wold's criterionselwold
Standard normal variate transformation (SNV)snv
Block dimension reduction by SO-PLSpredict.Soplsr soplsr soplsrcv transform.Soplsr
SOPLSR or SOPLSDA analysis stepssoplsr_soplsda_allsteps
Block dimension reduction by SO-PLS-DApredict.Soplsprobda predict.Soplsrda soplslda soplsldacv soplsqda soplsqdacv soplsrda soplsrdacv transform.Soplsprobda transform.Soplsrda
Source R functions in a directorysourcedir
Description of the quantitative variables of a data setsumm
SVM Regression and Discriminationpredict.Svm summary.Svm svmda svmr
Generic transform functiontransform
Variable Importance in Projection (VIP)vip
Distance-based weightswdist
Matrix fitting from a PCA or PLS modelxfit xfit.Pca xfit.Plsr xresid