Package: MXM 1.5.5
Konstantina Biza
MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks
Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets. Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>. b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. <doi:10.1186/s12859-018-2023-7>. c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1): 19-30. <doi:10.1007/s41060-018-0097-y>. d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. <doi:10.1101/431734>. e) Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>. f) Tsagris, M. and Tsamardinos, I. (2019). Feature selection with the R package MXM. F1000Research 7: 1505. <doi:10.12688/f1000research.16216.2>. g) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39. h) The gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214-1224. <doi:10.1109/TCBB.2020.3029952>.
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MXM_1.5.5.tar.gz
MXM_1.5.5.tar.gz(r-4.5-noble)MXM_1.5.5.tar.gz(r-4.4-noble)
MXM_1.5.5.tgz(r-4.4-emscripten)MXM_1.5.5.tgz(r-4.3-emscripten)
MXM.pdf |MXM.html✨
MXM/json (API)
# Install 'MXM' in R: |
install.packages('MXM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:46a6170617. Checks:ERROR: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | FAIL | Dec 15 2024 |
R-4.5-linux | NOTE | Dec 15 2024 |
Exports:acc_multinom.mxmacc.mxmaucauc.mxmbbcbeta.modbeta.mxmbeta.regsbic.fsregbic.gammafsregbic.glm.fsregbic.mm.fsregbic.normlog.fsregbig.fbed.regbig.gompbig.score.univregsbn.skel.utilsbn.skel.utils2boot.gompbs.regcat.cicensIndCRcensIndERcensIndLLRcensIndWRcertificate.of.exclusioncertificate.of.exclusion2ci.fastci.fast2ci.mmci.mm2ci.mxmciwr.mxmclogit.mxmcond.regscondicondisconf.edge.lowercor.drop1corfbed.networkcorfs.networkcorgraphcoxph.mxmcv.fbed.lmm.regcv.gompcv.mmpccv.permmmpccv.permsescv.sescv.waldmmpccv.waldsesdag2egdist.condiebic.bsregebic.glmm.bsregebic.modelebic.regsebic.univregsequivdagseuclid_sens.spec.mxmfbed.gee.regfbed.glmm.regfbed.regfbedreg.bicfindAncestorsfindDescendantsfs.regfscore.mxmgammafsreggee.ci.mmgee.condregsgee.mmhc.skelgee.pc.skelgee.univregsgeneratefoldsglm.bsregglm.bsreg2glm.fsregglm.mxmglmm.bsregglmm.ci.mmglmm.condregsglmm.mmhc.skelglmm.pc.skelglmm.univregsgompgomp.pathgroup.mvbetasgSquareiambiamb.bsidaInternalMMPCInternalMMPC.geeInternalMMPC.glmmInternalSESInternalSES.geeInternalSES.glmmis.daglm.fsreglm.mxmlmrob.mxmlocal.mmhc.skellogiquant.regsma.mmpcma.sesmae.mxmmbmci.mxmmmhc.skelmmmbMMPCMMPC.geemmpc.gee.modelmmpc.gee2MMPC.glmmmmpc.glmm.modelmmpc.glmm2mmpc.modelmmpc.ormmpc.pathMMPC.timeclassmmpc.timeclass.modelmmpc2mmpcbackphasemodelermse.mxmmultinom.mxmnb.mxmnbdev.mxmneiNessnormlog.fsregord_mae.mxmord.residordinal.mxmordinal.regpartialcorpc.conpc.orpc.selpc.skelpc.skel.bootperm.betaregsperm.mmpcperm.mmpc.pathperm.sesperm.univregsperm.zipregspermBetapermBinompermClogitpermcorpermcorrelspermCRpermDcorpermERpermFisherpermGammapermgSquarepermIGregpermLLRpermLogisticpermMMFisherpermMMRegpermMultinompermMVregpermNBpermNormLogpermOrdinalpermPoispermRegpermRQpermTobitpermWRpermZIPpi0estplotplotnetworkpois.mxmpoisdev.mxmprec.mxmpval.mixbetapve.mxmrdagrdag2read.big.datareg.fitridge.plotridge.regridgereg.cvrint.regsrmdagrq.mxmscore.univregssens.mxmSESSES.geeses.gee.modelSES.glmmses.glmm.modelses.modelSES.timeclassses.timeclass.modelshdsp.logiregsspec.mxmsupervised.pcatc.plottestIndBetatestIndBinomtestIndClogittestIndFishertestIndGammatestIndGEEGammatestIndGEELogistictestIndGEENormLogtestIndGEEPoistestIndGEERegtestIndGLMMCRtestIndGLMMGammatestIndGLMMLogistictestIndGLMMNBtestIndGLMMNormLogtestIndGLMMOrdinaltestIndGLMMPoistestIndGLMMRegtestIndIGregtestIndLMMtestIndLogistictestIndMMFishertestIndMMRegtestIndMultinomtestIndMVregtestIndNBtestIndNormLogtestIndOrdinaltestIndPoistestIndQBinomtestIndQPoistestIndRegtestIndRQtestIndSpearmantestIndSPMLtestIndTimeLogistictestIndTimeMultinomtestIndTobittestIndZIPtopological_sorttransitiveClosuretriangles.searchundir.pathunivregswald.betaregswald.logisticregswald.mmpcwald.mmpc.pathwald.poissonregswald.seswald.univregswald.zipregswaldBetawaldBinomwaldCRwaldERwaldGammawaldIGregwaldLLRwaldLogisticwaldmmpc.modelwaldMMRegwaldNBwaldNormLogwaldOrdinalwaldPoiswaldses.modelwaldTobitwaldWRwaldZIPweibreg.mxmzinb.modzinb.regzip.modzip.regzip.regs
Dependencies:backportsbase64encbdsmatrixBHbigmemorybigmemory.sribootbroombslibcachemcheckmatecliclustercodetoolscolorspacecoxmecpp11data.tabledigestdoParalleldplyrenergyevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsgeepackgenericsggplot2gluegridExtragslgtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmimeminqamunsellnlmenloptrnnetnumDerivordinalpillarpkgconfigpurrrquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratrelationsRfastRfast2rlangrmarkdownRnanoflannrpartrstudioapisassscalessetsslamSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexucminfutf8uuidvctrsviridisviridisLitevisNetworkwithrxfunyaml
A very brief guide to using MXM
Rendered fromguide.ltx
usingR.rsp::tex
on Dec 15 2024.Last update: 2019-12-06
Started: 2017-10-06
Discovering Statistically-Equivalent Feature Subsets with MXM
Rendered fromarticle.ltx
usingR.rsp::tex
on Dec 15 2024.Last update: 2018-05-24
Started: 2016-12-20
Guide on performing feature selection with the R package MXM
Rendered fromFS_guide.ltx
usingR.rsp::tex
on Dec 15 2024.Last update: 2019-12-06
Started: 2018-05-24
Tutorial: Feature selection with the MMPC algorithm
Rendered fromMMPC_tutorial.Rmd
usingknitr::knitr
on Dec 15 2024.Last update: 2021-09-21
Started: 2018-09-19
Tutorial: Feature selection with the SES algorithm
Rendered fromSES_KMVerrou_11_12.Rmd
usingknitr::knitr
on Dec 15 2024.Last update: 2021-09-21
Started: 2018-03-30