{ "_id": "6aca84d5fe5cc25786e8abaf", "Package": "MXM", "Type": "Package", "Title": "Feature Selection (Including Multiple Solutions) and Bayesian\nNetworks", "Version": "1.5.8", "Date": "2026-07-21", "Authors@R": "c( person(\"Konstantina\", \"Biza\", role = c(\"aut\"), email = \"kbiza@csd.uoc.gr\"), \nperson(\"Ioannis\", \"Tsamardinos\", role = c(\"aut\", \"cph\"), email = \"tsamard.it@gmail.com\"),\nperson(\"Vincenzo\", \"Lagani\", role = c(\"aut\", \"cph\"), email = \"vlagani@yahoo.it\"),\nperson(\"Giorgos\", \"Athineou\", role = c(\"aut\"), email = \"gioathineou@gmail.com\"),\nperson(\"Michail\", \"Tsagris\", role = c(\"aut\"), email = \"mtsagris@uoc.gr\"),\nperson(\"Giorgos\", \"Borboudakis\", role = c(\"ctb\"), email = \"borbudak@gmail.com\"),\nperson(\"Anna\", \"Roumpelaki\", role = c(\"ctb\"), email = \"anna.roumpelaki@gmail.com\"),\nperson(\"Stavros\", \"Papadopoulos\", role = c(\"cre\"), email = \"staurospapflor@gmail.com\") )", "Author": "Konstantina Biza [aut], Ioannis Tsamardinos [aut, cph],\nVincenzo Lagani [aut, cph], Giorgos Athineou [aut], Michail\nTsagris [aut], Giorgos Borboudakis [ctb], Anna Roumpelaki\n[ctb], Stavros Papadopoulos [cre]", "Maintainer": "Stavros Papadopoulos ", "Description": "Many feature selection methods for a wide range of\nresponse variables, including minimal, statistically-equivalent\nand equally-predictive feature subsets. Bayesian network\nalgorithms and related functions are also included. The package\nname 'MXM' stands for \"Mens eX Machina\", meaning \"Mind from the\nMachine\" in Latin. References: a) Lagani, V. and Athineou, G.\nand Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017).\n\"Feature Selection with the R Package MXM: Discovering\nStatistically Equivalent Feature Subsets\". Journal of\nStatistical Software, 80(7). . b)\nTsagris, M., Lagani, V. and Tsamardinos, I. (2018). \"Feature\nselection for high-dimensional temporal data\". BMC\nBioinformatics, 19:17. . c)\nTsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I.\n(2018). \"Constraint-based causal discovery with mixed data\".\nInternational Journal of Data Science and Analytics, 6(1):\n19-30. . d) Tsagris, M.,\nPapadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018).\n\"Efficient feature selection on gene expression data: Which\nalgorithm to use?\" BioRxiv. . e) Tsagris,\nM. (2019). \"Bayesian Network Learning with the PC Algorithm: An\nImproved and Correct Variation\". Applied Artificial\nIntelligence, 33(2):101-123.\n. f) Tsagris, M. and\nTsamardinos, I. (2019). \"Feature selection with the R package\nMXM\". F1000Research 7: 1505.\n. g) Borboudakis, G. and\nTsamardinos, I. (2019). \"Forward-Backward Selection with Early\nDropping\". Journal of Machine Learning Research 20: 1-39. h)\nTsagris, M., Papadovasilakis, Z., Lakiotaki, K. and\nTsamardinos, I. (2022). \"The gamma-OMP algorithm for feature\nselection with application to gene expression data\". 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"waldTobit" ] }, { "page": "testIndGamma", "title": "Regression conditional independence test for positive response variables.", "topics": [ "permGamma", "permIGreg", "permNormLog", "testIndGamma", "testIndIGreg", "testIndNormLog", "waldGamma", "waldIGreg", "waldNormLog" ] }, { "page": "testIndBinom", "title": "Binomial regression conditional independence test for success rates (binomial)", "topics": [ "permBinom", "testIndBinom", "waldBinom" ] }, { "page": "censIndCR", "title": "Conditional independence test for survival data", "topics": [ "censIndCR", "censIndER", "censIndLLR", "censIndWR", "permCR", "permER", "permLLR", "permWR", "waldCR", "waldER", "waldLLR", "waldWR" ] }, { "page": "condi", "title": "Conditional independence test for continuous class variables with and without permutation based p-value", "topics": [ "cat.ci", "condi", "dist.condi" ] }, { "page": "SES", "title": "SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets", "topics": [ "MMPC", "perm.mmpc", "perm.ses", "SES", "wald.mmpc", "wald.ses" ] }, { "page": "SES.glmm", "title": "SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data", "topics": [ "MMPC.gee", "MMPC.glmm", "SES.gee", "SES.glmm" ] }, { "page": "ma.ses", "title": "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 datasets", "topics": [ "ma.mmpc", "ma.ses" ] }, { "page": "testIndFisher", "title": "Fisher and Spearman conditional independence test for continuous class variables", "topics": [ "permDcor", "permFisher", "permMMFisher", "testIndFisher", "testIndMMFisher", "testIndSpearman" ] }, { "page": "cv.gomp", "title": "Cross-Validation for gOMP", "topics": [ "cv.gomp" ] }, { "page": "ridgereg.cv", "title": "Cross validation for the ridge regression", "topics": [ "ridgereg.cv" ] }, { "page": "cv.ses", "title": "Cross-Validation for SES and MMPC", "topics": [ "acc.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" ] }, { "page": "cv.fbed.lmm.reg", "title": "Cross-validation of the FBED with LMM", "topics": [ "cv.fbed.lmm.reg" ] }, { "page": "rdag", "title": "Data simulation from a DAG.", "topics": [ "rdag", "rdag2", "rmdag" ] }, { "page": "cor.drop1", "title": "Drop all possible single terms from a model using the partial correlation", "topics": [ "cor.drop1" ] }, { "page": "ebic.regs", "title": "eBIC for many regression models", "topics": [ "ebic.regs" ] }, { "page": "Ness", "title": "Effective sample size for G^2 test in BNs with case control data", "topics": [ "Ness" ] }, { "page": "pi0est", "title": "Estimation of the percentage of Null p-values", "topics": [ "pi0est" ] }, { "page": "mmpc2", "title": "A fast version of MMPC", "topics": [ "mmpc2" ] }, { "page": "mmpc.glmm2", "title": "mmpc.glmm2/mmpc.gee2: Fast Feature selection algorithm for identifying minimal feature subsets with correlated data", "topics": [ "mmpc.gee2", "mmpc.glmm2" ] }, { "page": "SES.timeclass", "title": "Feature selection using SES and MMPC for classifiication with longitudinal data", "topics": [ "MMPC.timeclass", "SES.timeclass" ] }, { "page": "pval.mixbeta", "title": "Fit a mixture of beta distributions in p-values", "topics": [ "pval.mixbeta" ] }, { "page": "fbed.reg", "title": "Forward Backward Early Dropping selection regression", "topics": [ "fbed.reg" ] }, { "page": "big.fbed.reg", "title": "Forward Backward Early Dropping selection regression for big data", "topics": [ "big.fbed.reg" ] }, { "page": "fbed.gee.reg", "title": "Forward Backward Early Dropping selection regression with GEE", "topics": [ "fbed.gee.reg" ] }, { "page": "fbed.glmm.reg", "title": "Forward Backward Early Dropping selection regression with GLMM", "topics": [ "fbed.glmm.reg" ] }, { "page": "fs.reg", "title": "Variable selection in regression models with forward selection", "topics": [ "fs.reg" ] }, { "page": "glm.fsreg", "title": "Variable selection in generalised linear regression models with forward selection", "topics": [ "gammafsreg", "glm.fsreg", "normlog.fsreg" ] }, { "page": "lm.fsreg", "title": "Variable selection in linear regression models with forward selection", "topics": [ "lm.fsreg" ] }, { "page": "gSquare", "title": "G-square conditional independence test for discrete data", "topics": [ "gSquare", "permgSquare" ] }, { "page": "mmpc.glmm.model", "title": "Generalised linear mixed model(s) based obtained from glmm SES or MMPC", "topics": [ "mmpc.gee.model", "mmpc.glmm.model", "ses.gee.model", "ses.glmm.model" ] }, { "page": "ordinal.reg", "title": "Generalised ordinal regression", "topics": [ "ordinal.reg" ] }, { "page": "generatefolds", "title": "Generate random folds for cross-validation", "topics": [ "generatefolds" ] }, { "page": "gomp", "title": "Generic orthogonal matching pursuit (gOMP)", "topics": [ "boot.gomp", "gomp", "gomp.path" ] }, { "page": "big.gomp", "title": "Generic orthogonal matching pursuit(gOMP) for big data", "topics": [ "big.gomp", "big.gomp.path" ] }, { "page": "corgraph", "title": "Graph of unconditional associations", "topics": [ "corgraph" ] }, { "page": "iamb.bs", "title": "IAMB backward selection phase", "topics": [ "iamb.bs" ] }, { "page": "iamb", "title": "IAMB variable selection", "topics": [ "iamb" ] }, { "page": "fbedreg.bic", "title": "Incremental BIC values and final regression model of the FBED algorithm", "topics": [ "fbedreg.bic" ] }, { "page": "plotnetwork", "title": "Interactive plot of an (un)directed graph", "topics": [ "plotnetwork" ] }, { "page": "conf.edge.lower", "title": "Lower limit of the confidence of an edge", "topics": [ "conf.edge.lower" ] }, { "page": "mammpc.output-class", "title": "Class '\"mammpc.output\"'", "topics": [ "mammpc.output", "mammpc.output-class", "mammpc.output-method", "plot,mammpc.output,ANY-method", "plot,mammpc.output-method" ] }, { "page": "sp.logiregs", "title": "Many approximate simple logistic regressions.", "topics": [ "sp.logiregs" ] }, { "page": "beta.regs", "title": "Many simple beta regressions.", "topics": [ "beta.regs", "perm.betaregs", "wald.betaregs" ] }, { "page": "logiquant.regs", "title": "Many simple quantile regressions using logistic regressions.", "topics": [ "logiquant.regs" ] }, { "page": "zip.regs", "title": "Many simple zero inflated Poisson regressions.", "topics": [ "perm.zipregs", "wald.zipregs", "zip.regs" ] }, { "page": "wald.logisticregs", "title": "Many Wald based tests for logistic and Poisson regressions with continuous predictors", "topics": [ "wald.logisticregs", "wald.poissonregs" ] }, { "page": "mb", "title": "Returns the Markov blanket of a node (or variable)", "topics": [ "mb" ] }, { "page": "mases.output-class", "title": "Class '\"mases.output\"'", "topics": [ "mases.output", "mases.output-class", "mases.output-method", "plot,mases.output,ANY-method", "plot,mases.output-method" ] }, { "page": "mmpc.path", "title": "MMPC solution paths for many combinations of hyper-parameters", "topics": [ "mmpc.path", "perm.mmpc.path", "wald.mmpc.path" ] }, { "page": "MMPC.gee.output-class", "title": "Class '\"MMPC.gee.output\"'", "topics": [ "MMPC.gee.output", "MMPC.gee.output-class", "MMPC.gee.output-method", "plot,MMPC.gee.output,ANY-method", "plot,MMPC.gee.output-method" ] }, { "page": "MMPC.glmm.output-class", "title": "Class '\"MMPC.glmm.output\"'", "topics": [ "MMPC.glmm.output", "MMPC.glmm.output-class", "MMPC.glmm.output-method", "plot,MMPC.glmm.output,ANY-method", "plot,MMPC.glmm.output-method" ] }, { "page": "MMPCoutput-class", "title": "Class '\"MMPCoutput\"'", "topics": [ "MMPCoutput", "MMPCoutput-class", "MMPCoutput-method", "plot,MMPCoutput,ANY-method", "plot,MMPCoutput-method" ] }, { "page": "MXM-internal", "title": "Internal MXM Functions", "topics": [ "apply_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" ] }, { "page": "nei", "title": "Returns the node(s) and their neighbour(s), if there are any.", "topics": [ "nei" ] }, { "page": "corfs.network", "title": "Network construction using the partial correlation based forward regression of FBED", "topics": [ "corfbed.network", "corfs.network" ] }, { "page": "pc.or", "title": "The orientations part of the PC algorithm.", "topics": [ "pc.or" ] }, { "page": "partialcor", "title": "Partial correlation", "topics": [ "partialcor" ] }, { "page": "permcor", "title": "Permutation based p-value for the Pearson correlation coefficient", "topics": [ "permcor", "permcorrels" ] }, { "page": "tc.plot", "title": "Plot of longitudinal data", "topics": [ "tc.plot" ] }, { "page": "ord.resid", "title": "Probability residual of ordinal logistic regreession", "topics": [ "ord.resid" ] }, { "page": "read.big.data", "title": "Read big data or a big.matrix object", "topics": [ "read.big.data" ] }, { "page": "modeler", "title": "Generic regression modelling function", "topics": [ "modeler" ] }, { "page": "ses.model", "title": "Regression model(s) obtained from SES or MMPC", "topics": [ "mmpc.model", "ses.model", "waldmmpc.model", "waldses.model" ] }, { "page": "mmpc.timeclass.model", "title": "Regression model(s) obtained from SES.timeclass or MMPC.timeclass", "topics": [ "mmpc.timeclass.model", "ses.timeclass.model" ] }, { "page": "reg.fit", "title": "Regression modelling", "topics": [ "reg.fit" ] }, { "page": "ridge.reg", "title": "Ridge regression", "topics": [ "ridge.reg" ] }, { "page": "ridge.plot", "title": "Ridge regression", "topics": [ "ridge.plot" ] }, { "page": "auc", "title": "ROC and AUC", "topics": [ "auc" ] }, { "page": "triangles.search", "title": "Search for triangles in an undirected graph", "topics": [ "triangles.search" ] }, { "page": "SES.gee.output-class", "title": "Class '\"SES.gee.output\"'", "topics": [ "plot,SES.gee.output,ANY-method", "plot,SES.gee.output-method", "SES.gee.output", "SES.gee.output-class", "SES.gee.output-method" ] }, { "page": "SES.glmm.output-class", "title": "Class '\"SES.glmm.output\"'", "topics": [ "plot,SES.glmm.output,ANY-method", "plot,SES.glmm.output-method", "SES.glmm.output", "SES.glmm.output-class", "SES.glmm.output-method" ] }, { "page": "SESoutput-class", "title": "Class '\"SESoutput\"'", "topics": [ "plot,SESoutput,ANY-method", "plot,SESoutput-method", "SESoutput", "SESoutput-class", "SESoutput-method" ] }, { "page": "local.mmhc.skel", "title": "Skeleton (local) around a node of the MMHC algorithm", "topics": [ "local.mmhc.skel" ] }, { "page": "mmhc.skel", "title": "The skeleton of a Bayesian network as produced by MMHC", "topics": [ "gee.mmhc.skel", "glmm.mmhc.skel", "mmhc.skel" ] }, { "page": "pc.skel", "title": "The skeleton of a Bayesian network produced by the PC algorithm", "topics": [ "gee.pc.skel", "glmm.pc.skel", "pc.con", "pc.skel", "pc.skel.boot" ] }, { "page": "shd", "title": "Structural Hamming distance between two partially oriented DAGs", "topics": [ "shd" ] }, { "page": "supervised.pca", "title": "Supervised PCA", "topics": [ "supervised.pca" ] }, { "page": "glmm.ci.mm", "title": "Symmetric conditional independence test with clustered data", "topics": [ "gee.ci.mm", "glmm.ci.mm" ] }, { "page": "ci.mm", "title": "Symmetric conditional independence test with mixed data", "topics": [ "ci.fast", "ci.fast2", "ci.mm", "ci.mm2" ] }, { "page": "mmmb", "title": "Max-min Markov blanket algorithm", "topics": [ "mmmb" ] }, { "page": "topological_sort", "title": "Topological sort of a DAG", "topics": [ "topological_sort" ] }, { "page": "ida", "title": "Total causal effect of a node on another node", "topics": [ "ida" ] }, { "page": "dag2eg", "title": "Transforms a DAG into an essential graph", "topics": [ "dag2eg" ] }, { "page": "transitiveClosure", "title": "Returns the transitive closure of an adjacency matrix", "topics": [ "transitiveClosure" ] }, { "page": "undir.path", "title": "Undirected path(s) between two nodes", "topics": [ "undir.path" ] }, { "page": "univregs", "title": "Univariate regression based tests", "topics": [ "big.score.univregs", "ebic.univregs", "gee.univregs", "glmm.univregs", "perm.univregs", "rint.regs", "score.univregs", "univregs", "wald.univregs" ] }, { "page": "bn.skel.utils", "title": "Utilities for the skeleton of a (Bayesian) Network", "topics": [ "bn.skel.utils", "bn.skel.utils2" ] }, { "page": "pc.sel", "title": "Variable selection using the PC-simple algorithm", "topics": [ "pc.sel" ] }, { "page": "zip.mod", "title": "Zero inflated Poisson and negative binomial regression", "topics": [ "zinb.mod", "zinb.reg", "zip.mod", "zip.reg" ] } ], "_rundeps": [ "backports", "base64enc", "bdsmatrix", "BH", "bigmemory", "bigmemory.sri", "boot", "broom", "bslib", "cachem", "checkmate", "cli", "cluster", "codetools", "colorspace", "coxme", "cpp11", "data.table", "digest", "doParallel", "dplyr", "energy", "evaluate", "farver", "fastmap", "fontawesome", "foreach", "foreign", "Formula", "fs", "geepack", "generics", "ggplot2", "glue", "gridExtra", "gsl", "gtable", "highr", "Hmisc", "htmlTable", "htmltools", "htmlwidgets", "isoband", 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14:30:18", "commits": 6 }, { "source": "article.ltx", "filename": "article.pdf", "title": "Discovering Statistically-Equivalent Feature Subsets with MXM", "engine": "R.rsp::tex", "headings": [ "Introduction", "Multiple signature selection with SES algorithm", "Package implementation", "Using SES", "Experimental validation", "Discussion and conclusions", "Acknowledgments" ], "created": "2016-12-20 09:46:49", "modified": "2026-09-01 14:59:12", "commits": 7 }, { "source": "FS_guide.ltx", "filename": "FS_guide.pdf", "title": "Guide on performing feature selection with the R package MXM", "engine": "R.rsp::tex", "headings": [ "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" ], "created": "2018-05-24 10:56:05", "modified": "2026-09-01 14:59:12", "commits": 6 }, { "source": "MMPC_tutorial.Rmd", "filename": "MMPC_tutorial.html", "title": "Tutorial: Feature 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"commits": 6 }, { "source": "SES_KMVerrou_11_12.Rmd", "filename": "SES_KMVerrou_11_12.html", "title": "Tutorial: Feature selection with the SES algorithm", "author": "Kleio-Maria Verrou and Michail Tsagris", "engine": "knitr::knitr", "headings": [ "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" ], "created": "2018-03-30 16:07:35", "modified": "2026-09-01 14:59:12", "commits": 8 } ], "_score": 4.8228216453031045, "_indexed": true, "_nocasepkg": "mxm", "_universes": [ "stavrospap1" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.5.8", "date": 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