Package: MXM 1.5.8

Stavros Papadopoulos

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) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2022). "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>.

Authors:Konstantina Biza [aut], Ioannis Tsamardinos [aut, cph], Vincenzo Lagani [aut, cph], Giorgos Athineou [aut], Michail Tsagris [aut], Giorgos Borboudakis [ctb], Anna Roumpelaki [ctb], Stavros Papadopoulos [cre]

MXM_1.5.8.tar.gz
MXM_1.5.8.zip(r-4.7-any)MXM_1.5.8.zip(r-4.6-any)MXM_1.5.8.zip(r-4.5-any)
MXM_1.5.8.tgz(r-4.6-any)MXM_1.5.8.tgz(r-4.5-any)
MXM_1.5.8.tar.gz(r-4.7-any)MXM_1.5.8.tar.gz(r-4.6-any)
MXM_1.5.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MXM/json (API)

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

On CRAN:

Conda:

archivedpackagesr-universe

5.52 score 5 stars 189 scripts 943 downloads 7 mentions 285 exports 108 dependencies

Last updated from:4722f7b29a (on package/MXM). Checks:8 OK, 1 ERROR. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK316
source / vignettesERROR394
linux-releaseOK311
macos-releaseOK268
macos-oldrelOK235
windows-develOK317
windows-releaseOK323
windows-oldrelOK312
wasm-releaseOK157

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.tabledigestdoParalleldplyrenergyevaluatefarverfastmapfontawesomeforeachforeignFormulafsgeepackgenericsggplot2gluegridExtragslgtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemimeminqanlmenloptrnnetnumDerivordinalpillarpkgconfigpurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackreformulasrelationsRfastRfast2rlangrmarkdownRnanoflannrpartrstudioapiS7sassscalessetsslamSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexucminfutf8uuidvctrsviridisLitevisNetworkwithrxfunyamlzigg

A very brief guide to using MXM
Introduction | Feature selection related functions | Networks | Acknowledgments
Rendered fromguide.ltxusingR.rsp::tex

Last update: 2026-11-03
Started: 2026-11-03

Discovering Statistically-Equivalent Feature Subsets with MXM
Introduction | Multiple signature selection with SES algorithm | Package implementation | Using SES | Experimental validation | Discussion and conclusions | Acknowledgments
Rendered fromarticle.ltxusingR.rsp::tex

Last update: 2026-11-03
Started: 2026-11-03

Guide on performing feature selection with the R package MXM
Introduction | Overview of the feature selection algorithms and types of data MXM can handle | Overview of the algorithms in R | Demonstration of the algorithms using real data
Rendered fromFS_guide.ltxusingR.rsp::tex

Last update: 2026-11-03
Started: 2026-11-03

Tutorial: Feature selection with the MMPC algorithm
Introduction: The MXM R package for Feature Selection | Overview of the MMPC algorithm | Selecting the appropriate Conditional Independence Test | The MMPC() function: Required and optional arguments | Applying the MMPC algorithm on the UCI wine dataset | Exploratory Data Analysis: Inspecting the wine dataset | Preparing the MMPC() input objects: 'target', 'dataset' | First run of MMPC() | MMPC() output: The mmpcobject and the feature signature | MMPC() subsequent run: Efficiency with the ini and hashObject arguments | Grid search for hyperparameter tuning: the mmpc.path() function | Optimized performance tips and tricks | Permutation option | Choosing the appropriate max_k for the conditioning set | Parallel computing: Choose number of cores option | References
Rendered fromMMPC_tutorial.Rmdusingknitr::knitr

Last update: 2026-11-03
Started: 2026-11-03

Tutorial: Feature selection with the SES algorithm
Introduction | Loading Data | SES for Continuous | Running SES for 1st time | Selecting Appropriate Conditional Independence Test | Creating Data & Target Matrices | Function's Arguments | Output | Running SES 2nd time | Creating the model | SES for Categorical | Running SES for Categorical | Setting the Arguments | Permutation Based SES | Cross - Validation | Tips | Choosing k_max | Choosing number of cores | Conclusion | Session Info | References
Rendered fromSES_KMVerrou_11_12.Rmdusingknitr::knitr

Last update: 2026-11-03
Started: 2026-11-03

Readme and manuals

Help Manual

Help pageTopics
This is an R package that currently implements feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. Additionally, the package includes two algorithms for constructing the skeleton of a Bayesian network.MXM-package
Returns and plots, if asked, the descendants or ancestors of one or all node(s) (or variable(s))findAncestors findDescendants
Backward phase of MMPCmmpcbackphase
Variable selection in regression models with backward selectionbs.reg
Backward selection regression for GLMMglmm.bsreg
Backward selection regression for GLMM using the eBICebic.glmm.bsreg
Backward selection regression using the eBICebic.bsreg
Variable selection in generalised linear regression models with backward selectionglm.bsreg glm.bsreg2
Bayesian Network construction using a hybrid of MMPC and PCmmpc.or
Beta regressionbeta.mod
Variable selection in regression models with forward selection using BICbic.fsreg
Variable selection in generalised linear models with forward selection based on BICbic.gammafsreg bic.glm.fsreg bic.mm.fsreg bic.normlog.fsreg
Bootstrap bias correction for the performance of the cross-validation procedurebbc
Calculation of the constant and slope for each subject over timegroup.mvbetas
Certificate of exclusion from the selected variables set using SES or MMPCcertificate.of.exclusion certificate.of.exclusion2
Check Markov equivalence of two DAGsequivdags
Check whether a directed graph is acyclicis.dag
MXM Conditional independence testsCondIndTests
Conditional independence regression based testscond.regs gee.condregs glmm.condregs
Conditional independence test for binary, categorical or ordinal class variablespermLogistic permMultinom permOrdinal testIndLogistic testIndMultinom testIndOrdinal testIndQBinom waldLogistic waldOrdinal waldQBinom
Conditional independence test based on conditional logistic regression for case control studiespermClogit testIndClogit
Circular regression conditional independence test for circular class dependent variables and continuous predictors.testIndSPML
Linear mixed models conditional independence test for longitudinal class variablestestIndGEEGamma testIndGEELogistic testIndGEENormLog testIndGEEPois testIndGEEReg
Linear mixed models conditional independence test for longitudinal class variablestestIndGLMMCR testIndGLMMGamma testIndGLMMLogistic testIndGLMMNB testIndGLMMNormLog testIndGLMMOrdinal testIndGLMMPois testIndGLMMReg testIndLMM
Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictorspermBeta testIndBeta waldBeta
Conditional independence test for the static-longitudinal scenariotestIndTimeLogistic testIndTimeMultinom
Many conditional independence tests counting the number of times a possible collider d-separates two nodescondis
Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variablespermMMReg permMVreg permReg permRQ testIndMMReg testIndMVreg testIndReg testIndRQ waldMMReg
Regression conditional independence test for discrete (counts) class dependent variablespermNB permPois permZIP testIndNB testIndPois testIndQPois testIndZIP waldNB waldPois waldZIP
Conditional independence test for survival datapermTobit testIndTobit waldTobit
Regression conditional independence test for positive response variables.permGamma permIGreg permNormLog testIndGamma testIndIGreg testIndNormLog waldGamma waldIGreg waldNormLog
Binomial regression conditional independence test for success rates (binomial)permBinom testIndBinom waldBinom
Conditional independence test for survival datacensIndCR censIndER censIndLLR censIndWR permCR permER permLLR permWR waldCR waldER waldLLR waldWR
Conditional independence test for continuous class variables with and without permutation based p-valuecat.ci condi dist.condi
SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsetsMMPC perm.mmpc perm.ses SES wald.mmpc wald.ses
SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated dataMMPC.gee MMPC.glmm SES.gee SES.glmm
ma.ses: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with multiple datasets ma.mmpc: Feature selection algorithm for identifying minimal feature subsets with multiple datasetsma.mmpc ma.ses
Fisher and Spearman conditional independence test for continuous class variablespermDcor permFisher permMMFisher testIndFisher testIndMMFisher testIndSpearman
Cross-Validation for gOMPcv.gomp
Cross validation for the ridge regressionridgereg.cv
Cross-Validation for SES and MMPCacc.mxm acc_multinom.mxm auc.mxm beta.mxm ci.mxm ciwr.mxm clogit.mxm coxph.mxm cv.mmpc cv.permmmpc cv.permses cv.ses cv.waldmmpc cv.waldses euclid_sens.spec.mxm exporeg.mxm fscore.mxm glm.mxm llrreg.mxm lm.mxm lmrob.mxm mae.mxm mci.mxm mse.mxm multinom.mxm nb.mxm nbdev.mxm ordinal.mxm ord_mae.mxm pois.mxm poisdev.mxm prec.mxm pve.mxm rq.mxm sens.mxm spec.mxm weibreg.mxm
Cross-validation of the FBED with LMMcv.fbed.lmm.reg
Data simulation from a DAG.rdag rdag2 rmdag
Drop all possible single terms from a model using the partial correlationcor.drop1
eBIC for many regression modelsebic.regs
Effective sample size for G^2 test in BNs with case control dataNess
Estimation of the percentage of Null p-valuespi0est
A fast version of MMPCmmpc2
mmpc.glmm2/mmpc.gee2: Fast Feature selection algorithm for identifying minimal feature subsets with correlated datammpc.gee2 mmpc.glmm2
Feature selection using SES and MMPC for classifiication with longitudinal dataMMPC.timeclass SES.timeclass
Fit a mixture of beta distributions in p-valuespval.mixbeta
Forward Backward Early Dropping selection regressionfbed.reg
Forward Backward Early Dropping selection regression for big databig.fbed.reg
Forward Backward Early Dropping selection regression with GEEfbed.gee.reg
Forward Backward Early Dropping selection regression with GLMMfbed.glmm.reg
Variable selection in regression models with forward selectionfs.reg
Variable selection in generalised linear regression models with forward selectiongammafsreg glm.fsreg normlog.fsreg
Variable selection in linear regression models with forward selectionlm.fsreg
G-square conditional independence test for discrete datagSquare permgSquare
Generalised linear mixed model(s) based obtained from glmm SES or MMPCmmpc.gee.model mmpc.glmm.model ses.gee.model ses.glmm.model
Generalised ordinal regressionordinal.reg
Generate random folds for cross-validationgeneratefolds
Generic orthogonal matching pursuit (gOMP)boot.gomp gomp gomp.path
Generic orthogonal matching pursuit(gOMP) for big databig.gomp big.gomp.path
Graph of unconditional associationscorgraph
IAMB backward selection phaseiamb.bs
IAMB variable selectioniamb
Incremental BIC values and final regression model of the FBED algorithmfbedreg.bic
Interactive plot of an (un)directed graphplotnetwork
Lower limit of the confidence of an edgeconf.edge.lower
Class '"mammpc.output"'mammpc.output mammpc.output-class mammpc.output-method plot,mammpc.output,ANY-method plot,mammpc.output-method
Many approximate simple logistic regressions.sp.logiregs
Many simple beta regressions.beta.regs perm.betaregs wald.betaregs
Many simple quantile regressions using logistic regressions.logiquant.regs
Many simple zero inflated Poisson regressions.perm.zipregs wald.zipregs zip.regs
Many Wald based tests for logistic and Poisson regressions with continuous predictorswald.logisticregs wald.poissonregs
Returns the Markov blanket of a node (or variable)mb
Class '"mases.output"'mases.output mases.output-class mases.output-method plot,mases.output,ANY-method plot,mases.output-method
MMPC solution paths for many combinations of hyper-parametersmmpc.path perm.mmpc.path wald.mmpc.path
Class '"MMPC.gee.output"'MMPC.gee.output MMPC.gee.output-class MMPC.gee.output-method plot,MMPC.gee.output,ANY-method plot,MMPC.gee.output-method
Class '"MMPC.glmm.output"'MMPC.glmm.output MMPC.glmm.output-class MMPC.glmm.output-method plot,MMPC.glmm.output,ANY-method plot,MMPC.glmm.output-method
Class '"MMPCoutput"'MMPCoutput MMPCoutput-class MMPCoutput-method plot,MMPCoutput,ANY-method plot,MMPCoutput-method
Internal MXM Functionsapply_ideq apply_ideq.glmm apply_ideq.ma beta.bsreg beta.fsreg beta.fsreg_2 beta.reg betamle.wei bic.betafsreg bic.clogit.fsreg bic.llr.fsreg bic.tobit.fsreg bic.wr.fsreg bic.zipfsreg big.bs.g2 big.fbed.g2 big.model bs.g2 bsreg.big cat_condis clogit.fsreg clogit.fsreg_2 comb_condis compare_p_values condi.perm cvlogit.cv.ses cvmmpc.par cvpermmmpc.par cvpermses.par cvses.par cvwaldmmpc.par cvwaldses.par dag_to_eg disctor_condis ebic.beta.bsreg ebic.cr.bsreg ebic.fbed.beta ebic.fbed.cr ebic.fbed.glm ebic.fbed.glmm ebic.fbed.glmm.cr ebic.fbed.glmm.ordinal ebic.fbed.glmm.ordinal.reps ebic.fbed.glmm.reps ebic.fbed.lm ebic.fbed.lmm ebic.fbed.lmm.reps ebic.fbed.mmreg ebic.fbed.multinom ebic.fbed.nb ebic.fbed.ordinal ebic.fbed.tobit ebic.fbed.wr ebic.fbed.zip ebic.glm.bsreg ebic.glmm.cr.bsreg ebic.glmm.ordinal.reps.bsreg ebic.glmm.reps.bsreg ebic.llr.bsreg ebic.lm.bsreg ebic.mm.bsreg ebic.model ebic.multinom.bsreg ebic.ordinal.bsreg ebic.spml.bsreg ebic.tobit.bsreg ebic.wr.bsreg ebic.zip.bsreg ebicScore fbed.ebic fbed.g2 fbed.geeglm fbed.geeglm.reps fbed.geelm fbed.geelm.reps fbed.glmm fbed.glmm.cr fbed.glmm.nb fbed.glmm.nb.reps fbed.glmm.ordinal fbed.glmm.ordinal.reps fbed.glmm.reps fbed.lmm fbed.lmm.reps fbed.lr fbed.ordgee fbed.ordgee.reps fs.reg_2 gammafsreg_2 glm.fsreg_2 glmm.cr.bsreg glmm.nb.bsreg glmm.nb.reps.bsreg glmm.ordinal.bsreg glmm.ordinal.reps.bsreg gomp2 iamb.betabs iamb.gammabs iamb.glmbs iamb.normlogbs iamb.tobitbs iamb.zipbs IdentifyEquivalence IdentifyEquivalence.gee IdentifyEquivalence.glmm IdentifyEquivalence.ma identifyTheEquivalent identifyTheEquivalent.gee identifyTheEquivalent.glmm identifyTheEquivalent.ma internaliamb.betabs internaliamb.binombs internaliamb.gammabs internaliamb.lmbs internaliamb.mmbs internaliamb.normlogbs internaliamb.poisbs internaliamb.tobitbs internaliamb.zipbs Internalmammpc Internalmases InternalMMPC InternalMMPC.gee InternalMMPC.glmm InternalMMPC.timeclass InternalSES InternalSES.gee InternalSES.glmm is.sepset kfbed.gee.reg kfbed.glmm.reg kfbed.reg llr.bsreg lm.fsreg_2 lmm.bsreg max_min_assoc max_min_assoc.gee max_min_assoc.glmm max_min_assoc.ma min_assoc min_assoc.gee min_assoc.glmm min_assoc.ma nchoosek pearson_condis pearson_condis.rob perm.apply_ideq perm.IdentifyEquivalence perm.identifyTheEquivalent perm.Internalmmpc perm.univariateScore proc_time-class quasibinom.fsreg quasibinom.fsreg_2 quasipois.fsreg quasipois.fsreg_2 R0 R1 R2 R3 regbeta regbetawei regzinb regzip regzipwei spml.bsreg test.maker univariateScore univariateScore.gee univariateScore.glmm univariateScore.ma univariateScore.timeclass wald.Internalmmpc wald.Internalses wald.univariateScore wr.fsreg wr.fsreg_2 zinb.mle zip.bsreg zip.fsreg zip.fsreg_2 zipmle.wei zipwei
Returns the node(s) and their neighbour(s), if there are any.nei
Network construction using the partial correlation based forward regression of FBEDcorfbed.network corfs.network
The orientations part of the PC algorithm.pc.or
Partial correlationpartialcor
Permutation based p-value for the Pearson correlation coefficientpermcor permcorrels
Plot of longitudinal datatc.plot
Probability residual of ordinal logistic regreessionord.resid
Read big data or a big.matrix objectread.big.data
Generic regression modelling functionmodeler
Regression model(s) obtained from SES or MMPCmmpc.model ses.model waldmmpc.model waldses.model
Regression model(s) obtained from SES.timeclass or MMPC.timeclassmmpc.timeclass.model ses.timeclass.model
Regression modellingreg.fit
Ridge regressionridge.reg
Ridge regressionridge.plot
ROC and AUCauc
Search for triangles in an undirected graphtriangles.search
Class '"SES.gee.output"'plot,SES.gee.output,ANY-method plot,SES.gee.output-method SES.gee.output SES.gee.output-class SES.gee.output-method
Class '"SES.glmm.output"'plot,SES.glmm.output,ANY-method plot,SES.glmm.output-method SES.glmm.output SES.glmm.output-class SES.glmm.output-method
Class '"SESoutput"'plot,SESoutput,ANY-method plot,SESoutput-method SESoutput SESoutput-class SESoutput-method
Skeleton (local) around a node of the MMHC algorithmlocal.mmhc.skel
The skeleton of a Bayesian network as produced by MMHCgee.mmhc.skel glmm.mmhc.skel mmhc.skel
The skeleton of a Bayesian network produced by the PC algorithmgee.pc.skel glmm.pc.skel pc.con pc.skel pc.skel.boot
Structural Hamming distance between two partially oriented DAGsshd
Supervised PCAsupervised.pca
Symmetric conditional independence test with clustered datagee.ci.mm glmm.ci.mm
Symmetric conditional independence test with mixed dataci.fast ci.fast2 ci.mm ci.mm2
Max-min Markov blanket algorithmmmmb
Topological sort of a DAGtopological_sort
Total causal effect of a node on another nodeida
Transforms a DAG into an essential graphdag2eg
Returns the transitive closure of an adjacency matrixtransitiveClosure
Undirected path(s) between two nodesundir.path
Univariate regression based testsbig.score.univregs ebic.univregs gee.univregs glmm.univregs perm.univregs rint.regs score.univregs univregs wald.univregs
Utilities for the skeleton of a (Bayesian) Networkbn.skel.utils bn.skel.utils2
Variable selection using the PC-simple algorithmpc.sel
Zero inflated Poisson and negative binomial regressionzinb.mod zinb.reg zip.mod zip.reg