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A B C D F G I L M N O P R S T U W
| 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 |
| acc_multinom.mxm | Cross-Validation for SES |
| apply_ideq | Internal MXM Functions |
| apply_ideq.temporal | Internal MXM Functions |
| auc.mxm | Cross-Validation for SES |
| beta.mxm | Cross-Validation for SES |
| bic.fsreg | Variable selection in regression models with forward selection using BIC |
| bic.glm.fsreg | Variable selection in generalised linear regression models with forward selection |
| cat.ci | Internal MXM Functions |
| censIndCR | Conditional independence test for survival data |
| censIndWR | Conditional independence test for survival data |
| ci.mxm | Cross-Validation for SES |
| ciwr.mxm | Cross-Validation for SES |
| compare_p_values | Internal MXM Functions |
| condi | Internal MXM Functions |
| condi.perm | Internal MXM Functions |
| CondIndTests | MXM Conditional Independence Tests |
| coxph.mxm | Cross-Validation for SES |
| cv.ses | Cross-Validation for SES |
| dag2eg | Transforms a DAG into an essential graph |
| findAncestors | Returns and plots, if asked, the ancestors of a node (or variable) |
| findDescendants | Returns and plots, if asked, the descendants of a node (or variable) |
| fs.reg | Variable selection in regression models with forward selection |
| 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.mxm | Cross-Validation for SES |
| gSquare | G square conditional independence test for discrete data |
| IdentifyEquivalence | Internal MXM Functions |
| IdentifyEquivalence.temporal | Internal MXM Functions |
| identifyTheEquivalent | Internal MXM Functions |
| identifyTheEquivalent.temporal | 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.mxm | Cross-Validation for SES |
| lmrob.mxm | Cross-Validation for SES |
| max_min_assoc | 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.temporal | Internal MXM Functions |
| mmhc.skel | The skeleton of a Bayesian network produced by MMHC |
| mmmb | mmmb: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures. |
| 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.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"' |
| model | Regression model(s) obtained from SES |
| mse.mxm | Cross-Validation for SES |
| multinom.mxm | Cross-Validation for SES |
| nb.mxm | Cross-Validation for SES |
| nbdev.mxm | Cross-Validation for SES |
| nchoosekm | 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 |
| ord_mae.mxm | Cross-Validation for SES |
| 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 |
| plot-method | Class '"MMPC.temporal.output"' |
| plot-method | Class '"MMPCoutput"' |
| plot-method | Class '"SES.temporal.output"' |
| plot-method | Class '"SESoutput"' |
| plota | Plot of an (un)directed graph |
| pois.mxm | Cross-Validation for SES |
| poisdev.mxm | Cross-Validation for SES |
| proc_time-class | Internal MXM Functions |
| rdag | G square conditional independence test for discrete data |
| reg.fit | Regression modelling |
| ridge.plot | Ridge regression |
| ridge.reg | Ridge regression |
| ridgereg.cv | Cross validation for the ridge regression |
| rlm.mxm | Cross-Validation for SES |
| rq.mxm | Cross-Validation for SES |
| 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.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"' |
| tc.plot | Plot of longitudinal data |
| testIndBeta | Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors |
| 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 |
| testIndLogistic | Conditional independence test for binary, categorical or ordinal class variables |
| testIndMVreg | 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 regression conditional independence test for continous univariate and multivariate response variables |
| testIndRQ | 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 |
| transitiveClosure | Returns the transitive closure of a graph. |
| undir.path | Undirected path(s) between two nodes. |
| univariateScore | Internal MXM Functions |
| univariateScore.temporal | Internal MXM Functions |
| weibreg.mxm | Cross-Validation for SES |