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A B C D E F G I L M N O P R S T U V W Z
| MXM-package | This is an R package that currently implements feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. In addition, two algorithms for constructing the skeleton of a Bayesian network are included. |
| acc.mxm | Cross-Validation for SES and MMPC |
| acc_multinom.mxm | Cross-Validation for SES and MMPC |
| apply_ideq | Internal MXM Functions |
| apply_ideq.ma | Internal MXM Functions |
| apply_ideq.temporal | Internal MXM Functions |
| auc | ROC and area under the curve |
| auc.mxm | Cross-Validation for SES and MMPC |
| beta.bsreg | Internal MXM Functions |
| beta.fsreg | Internal MXM Functions |
| beta.mod | Beta regression |
| beta.mxm | Cross-Validation for SES and MMPC |
| beta.reg | Beta regression |
| beta.regs | Many simple beta regressions. |
| betamle.wei | Internal MXM Functions |
| bic.betafsreg | Internal MXM Functions |
| bic.fsreg | Variable selection in regression models with forward selection using BIC |
| bic.glm.fsreg | Variable selection in generalised linear models with forward selection based on BIC |
| bic.zipfsreg | Internal MXM Functions |
| bs.reg | Variable selection in regression models with backward selection |
| cat.ci | Internal MXM Functions |
| censIndCR | Conditional independence test for survival data |
| censIndER | Conditional independence test for survival data |
| censIndWR | Conditional independence test for survival data |
| ci.mxm | Cross-Validation for SES and MMPC |
| ciwr.mxm | Cross-Validation for SES and MMPC |
| compare_p_values | Internal MXM Functions |
| condi | Conditional independence test for continuous class variables with and without permutation based p-value |
| condi.perm | Internal MXM Functions |
| CondIndTests | MXM Conditional independence tests |
| coxph.mxm | Cross-Validation for SES and MMPC |
| cv.mmpc | Cross-Validation for SES and MMPC |
| cv.ses | Cross-Validation for SES and MMPC |
| cvmmpc.par | Internal MXM Functions |
| cvses.par | Internal MXM Functions |
| dag2eg | Transforms a DAG into an essential graph |
| dag_to_eg | Internal MXM Functions |
| dist.condi | Conditional independence test for continuous class variables with and without permutation based p-value |
| equivdags | Check Markov equivalence of two DAGs |
| findAncestors | Returns and plots, if asked, the descendants or ancestors of one or all node(s) (or variable(s)) |
| findDescendants | Returns and plots, if asked, the descendants or ancestors of one or all node(s) (or variable(s)) |
| fs.reg | Variable selection in regression models with forward selection |
| generatefolds | Generate random folds for cross-validation |
| glm.bsreg | Variable selection in generalised linear regression models with backward selection |
| glm.fsreg | Variable selection in generalised linear regression models with forward selection |
| glm.fsreg_2 | Internal MXM Functions |
| glm.mxm | Cross-Validation for SES and MMPC |
| gSquare | G-square conditional independence test for discrete data |
| iamb | IAMB variable selection |
| iamb.betabs | Internal MXM Functions |
| iamb.bs | IAMB backward selection phase |
| iamb.glmbs | Internal MXM Functions |
| iamb.zipbs | Internal MXM Functions |
| IdentifyEquivalence | Internal MXM Functions |
| IdentifyEquivalence.ma | Internal MXM Functions |
| IdentifyEquivalence.temporal | Internal MXM Functions |
| identifyTheEquivalent | Internal MXM Functions |
| identifyTheEquivalent.ma | Internal MXM Functions |
| identifyTheEquivalent.temporal | Internal MXM Functions |
| internaliamb.binombs | Internal MXM Functions |
| internaliamb.lmbs | Internal MXM Functions |
| internaliamb.poisbs | Internal MXM Functions |
| Internalmammpc | Internal MXM Functions |
| Internalmases | Internal MXM Functions |
| InternalMMPC | Internal MXM Functions |
| InternalMMPC.temporal | Internal MXM Functions |
| InternalSES | Internal MXM Functions |
| InternalSES.temporal | Internal MXM Functions |
| is.sepset | Internal MXM Functions |
| lm.fsreg | Variable selection in linear regression models with forward selection |
| lm.fsreg_2 | Internal MXM Functions |
| lm.fsreg_2.heavy | Internal MXM Functions |
| lm.fsreg_heavy | Variable selection in linear regression models with forward selection |
| lm.mxm | Cross-Validation for SES and MMPC |
| lmrob.mxm | Cross-Validation for SES and MMPC |
| ma.mmpc | 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 |
| ma.ses | 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 |
| mammpc.output | Class '"mammpc.output"' |
| mammpc.output-class | Class '"mammpc.output"' |
| mases.output | Class '"mases.output"' |
| mases.output-class | Class '"mases.output"' |
| max_min_assoc | Internal MXM Functions |
| max_min_assoc.ma | Internal MXM Functions |
| max_min_assoc.temporal | Internal MXM Functions |
| mb | Returns and plots, if asked, the Markov blanket of a node (or variable) |
| min_assoc | Internal MXM Functions |
| min_assoc.ma | Internal MXM Functions |
| min_assoc.temporal | Internal MXM Functions |
| mmhc.skel | The skeleton of a Bayesian network as produced by MMHC |
| mmmb | Max-min Markov blanket algorithm |
| MMPC | SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets |
| mmpc.model | Regression model(s) obtained from SES or MMPC |
| mmpc.path | MMPC solution paths for many combinations of hyper-parameters |
| MMPC.temporal | SES.temporal: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC.temporal: Feature selection algorithm for identifying minimal feature subsets |
| MMPC.temporal.output | Class '"MMPC.temporal.output"' |
| MMPC.temporal.output-class | Class '"MMPC.temporal.output"' |
| MMPCoutput | Class '"MMPCoutput"' |
| MMPCoutput-class | Class '"MMPCoutput"' |
| mse.mxm | Cross-Validation for SES and MMPC |
| multinom.mxm | Cross-Validation for SES and MMPC |
| nb.mxm | Cross-Validation for SES and MMPC |
| nbdev.mxm | Cross-Validation for SES and MMPC |
| nchoosek | Internal MXM Functions |
| nei | Returns and plots, if asked, the node(s) and their neighbour(s), if there are any. |
| ordinal.mxm | Cross-Validation for SES and MMPC |
| ord_mae.mxm | Cross-Validation for SES and MMPC |
| partialcor | Partial correlation |
| pc.con | The skeleton of a Bayesian network produced by the PC algorithm |
| pc.or | The orientations part of the PC algorithm. |
| pc.skel | The skeleton of a Bayesian network produced by the PC algorithm |
| permcor | Permutation based p-value for the Pearson correlation coefficient |
| permcorrels | Permutation based p-value for the Pearson correlation coefficient |
| permFisher | Fisher and Spearman conditional independence test for continuous class variables |
| plot-method | Class '"MMPC.temporal.output"' |
| plot-method | Class '"MMPCoutput"' |
| plot-method | Class '"SES.temporal.output"' |
| plot-method | Class '"SESoutput"' |
| plot-method | Class '"mammpc.output"' |
| plot-method | Class '"mases.output"' |
| plotnetwork | Interactive plot of an (un)directed graph |
| pois.mxm | Cross-Validation for SES and MMPC |
| poisdev.mxm | Cross-Validation for SES and MMPC |
| proc_time-class | Internal MXM Functions |
| R0 | Internal MXM Functions |
| R1 | Internal MXM Functions |
| R2 | Internal MXM Functions |
| R3 | Internal MXM Functions |
| rdag | Simulation of data from DAG (directed acyclic graph) |
| reg.fit | Regression modelling |
| regbeta | Internal MXM Functions |
| regbetawei | Internal MXM Functions |
| regzip | Internal MXM Functions |
| regzipwei | Internal MXM Functions |
| ridge.plot | Ridge regression |
| ridge.reg | Ridge regression |
| ridgereg.cv | Cross validation for the ridge regression |
| rq.mxm | Cross-Validation for SES and MMPC |
| SES | SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets |
| ses.model | Regression model(s) obtained from SES or MMPC |
| SES.temporal | SES.temporal: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC.temporal: Feature selection algorithm for identifying minimal feature subsets |
| SES.temporal.output | Class '"SES.temporal.output"' |
| SES.temporal.output-class | Class '"SES.temporal.output"' |
| SESoutput | Class '"SESoutput"' |
| SESoutput-class | Class '"SESoutput"' |
| summary-method | Class '"MMPC.temporal.output"' |
| summary-method | Class '"MMPCoutput"' |
| summary-method | Class '"SES.temporal.output"' |
| summary-method | Class '"SESoutput"' |
| summary-method | Class '"mammpc.output"' |
| summary-method | Class '"mases.output"' |
| tc.plot | Plot of longitudinal data |
| testIndBeta | Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors |
| testIndBinom | Binomial regression conditional independence test for success rates (binomial) |
| testIndClogit | Conditional independence test based on conditional logistic regression for case control studies |
| testIndFisher | Fisher and Spearman conditional independence test for continuous class variables |
| testIndGLMM | Linear mixed models conditional independence test for longitudinal class variables |
| testIndIGreg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| testIndLogistic | Conditional independence test for binary, categorical or ordinal class variables |
| testIndMVreg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| testIndNB | Regression conditional independence test for discrete (counts) class dependent variables |
| testIndPois | Regression conditional independence test for discrete (counts) class dependent variables |
| testIndReg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| testIndRQ | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| testIndSpearman | Fisher and Spearman conditional independence test for continuous class variables |
| testIndSpeedglm | Conditional independence test for continuous, binary and discrete (counts) variables with thousands of observations |
| testIndZIP | Regression conditional independence test for discrete (counts) class dependent variables |
| topological_sort | Internal MXM Functions |
| transitiveClosure | Returns the transitive closure of an adjacency matrix |
| undir.path | Undirected path(s) between two nodes |
| univariateScore | Internal MXM Functions |
| univariateScore.ma | Internal MXM Functions |
| univariateScore.temporal | Internal MXM Functions |
| univregs | Univariate regression based tests |
| vara | Internal MXM Functions |
| weibreg.mxm | Cross-Validation for SES and MMPC |
| zip.bsreg | Internal MXM Functions |
| zip.fsreg | Internal MXM Functions |
| zip.mod | Zero inflated Poisson regression |
| zip.reg | Zero inflated Poisson regression |
| zip.regs | Many simple zero inflated Poisson regressions. |
| zipmle.wei | Internal MXM Functions |
| zipwei | Internal MXM Functions |