Package: CompositionalSR 1.4

Michail Tsagris

CompositionalSR: Spatial Regression Models with Compositional Data

Spatial and non-spatial regression models with compositional responses (and compositional predictors) using the alpha--transformation. Relevant papers include: Tsagris M. and Pantazis Y. (2026), <doi:10.48550/arXiv.2510.12663>, Tsagris M. (2015), <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>, Tsagris M.T., Preston S. and Wood A.T.A. (2011), <doi:10.48550/arXiv.1106.1451>.

Authors:Michail Tsagris [aut, cre]

CompositionalSR_1.4.tar.gz
CompositionalSR_1.4.tar.gz(r-4.7-any)CompositionalSR_1.4.tar.gz(r-4.6-any)
CompositionalSR_1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
CompositionalSR/json (API)

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

On CRAN:

Conda:

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

1.70 score 396 downloads 31 exports 133 dependencies

Last updated from:33f7d09586. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK253
source / vignettesOK283
linux-release-x86_64OK251
wasm-releaseOK202

Exports:alfa.esfalfa.pcregalfa.regalfa.reg2alfa.reg3alfa.saralfa.slxalfa.slx2alfareg.nrar.gradsaregasar.gradsaslx.gradscontiguitycv.alfaesfcv.alfapcregcv.alfaregcv.alfasarcv.alfaslxcv.gwargwargwar.predice.aesfice.aregme.aesfme.arme.asarme.aslxme.gwarrob.alfaregspat.folds

Dependencies:abindautomapbase64encBHbigassertrbigparallelrbigstatsrbitblockCVbootbslibcachemclassclassIntcliclustercodetoolsCompositionalcowplotcpp11DBIdeldirdigestdoParalleldotCall64e1071emplikevaluatefarverfastmapfffieldsflockFNNfontawesomeforeachfsggplot2glmnetgluegslnlsgstatgtablehighrhtmltoolshtmlwidgetsintervalsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelifecyclemagrittrmapsMASSMatrixMatrixModelsmdamemoisemgcvmimeminpack.lmmixturemnormtnlmennetnumDerivosqpparallellypermutepillarpkgconfigplyrproxypsquadprogquantregR6rangenrappdirsrARPACKRColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelreshapeRfastRfast2rglRhpcBLASctlrlangrmarkdownrmioRnanoflannRSpectras2S7sassscalessfsftimeshapesnspspacetimespamSparseMspDataspdepspmoranstarssurvivalterratibbletinytexunitsutf8vctrsveganviridisLitewithrwkxfunxtsyamlziggzoo

Readme and manuals

Help Manual

Help pageTopics
Spatial Regression Models with Compositional DataCompositionalSR-package
Compositional regression with compositional predictors using the alpha-transformationalfa.pcreg
Computation of the contiguity matrix Wcontiguity
FADN datasetfadn
ICE plot for the alpha-ESF modelice.aesf
ICE plot for the alpha-regressionice.areg
K-fold cross-validation for the alpha-regressioncv.alfareg
K-fold cross-validation the alpha-regression with compositional predictorscv.alfapcreg
Leave-one-out cross-validation for the GWalphaR modelcv.gwar
Marginal effects for the alpha-ESF modelme.aesf
Marginal effects for the alpha-regression modelme.ar
Marginal effects for the alpha-SAR modelme.asar
Marginal effects for the alpha-SLX modelme.aslx
Marginal effects for the GWalphaR modelme.gwar
Prediction with the GWalphaR modelgwar.pred
Regression with compositional data using the alpha-transformationalfa.reg alfa.reg2 alfa.reg3 areg
Regression with compositional data using the alpha-transformationrob.alfareg
Spatial K-fold cross-validation for the alpha-ESF modelcv.alfaesf
Spatial K-fold cross-validation for the alpha-SAR modelcv.alfasar
Spatial K-fold cross-validation for the alpha-SLX modelcv.alfaslx
Spatial k-foldsspat.folds
The alpha-ESF modelalfa.esf
The alpha-regression using Newton-Raphsonalfareg.nr
The alpha-SAR modelalfa.sar
The alpha-SLX modelalfa.slx alfa.slx2
The gradient vector of the alpha-regression model at each observationar.grads
The gradient vector of the alpha-SAR model at each observationasar.grads
The gradient vector of the alpha-SLX model at each observationaslx.grads
The GWalphaR modelgwar