| cat.ci |
Conditional independence test for continuous class variables with and without permutation based p-value |
| cat_condis |
Internal MXM Functions |
| censIndCR |
Conditional independence test for survival data |
| censIndER |
Conditional independence test for survival data |
| censIndLLR |
Conditional independence test for survival data |
| censIndWR |
Conditional independence test for survival data |
| certificate.of.exclusion |
Certificate of exclusion from the selected variables set using SES or MMPC |
| certificate.of.exclusion2 |
Certificate of exclusion from the selected variables set using SES or MMPC |
| ci.fast |
Symmetric conditional independence test with mixed data |
| ci.fast2 |
Symmetric conditional independence test with mixed data |
| ci.mm |
Symmetric conditional independence test with mixed data |
| ci.mm2 |
Symmetric conditional independence test with mixed data |
| ci.mxm |
Cross-Validation for SES and MMPC |
| ciwr.mxm |
Cross-Validation for SES and MMPC |
| clogit.fsreg |
Internal MXM Functions |
| clogit.fsreg_2 |
Internal MXM Functions |
| clogit.mxm |
Cross-Validation for SES and MMPC |
| comb_condis |
Internal MXM Functions |
| compare_p_values |
Internal MXM Functions |
| cond.regs |
Conditional independence regression based tests |
| 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 |
| condis |
Many conditional independence tests counting the number of times a possible collider d-separates two nodes |
| conf.edge.lower |
Lower limit of the confidence of an edge |
| cor.drop1 |
Drop all possible single terms from a model using the partial correlation |
| corfbed.network |
Network construction using the partial correlation based forward regression of FBED |
| corfs.network |
Network construction using the partial correlation based forward regression of FBED |
| corgraph |
Graph of unconditional associations |
| coxph.mxm |
Cross-Validation for SES and MMPC |
| cv.fbed.lmm.reg |
Cross-validation of the FBED with LMM |
| cv.gomp |
Cross-Validation for gOMP |
| cv.mmpc |
Cross-Validation for SES and MMPC |
| cv.permmmpc |
Cross-Validation for SES and MMPC |
| cv.permses |
Cross-Validation for SES and MMPC |
| cv.ses |
Cross-Validation for SES and MMPC |
| cv.waldmmpc |
Cross-Validation for SES and MMPC |
| cv.waldses |
Cross-Validation for SES and MMPC |
| cvlogit.cv.ses |
Internal MXM Functions |
| cvmmpc.par |
Internal MXM Functions |
| cvpermmmpc.par |
Internal MXM Functions |
| cvpermses.par |
Internal MXM Functions |
| cvses.par |
Internal MXM Functions |
| cvwaldmmpc.par |
Internal MXM Functions |
| cvwaldses.par |
Internal MXM Functions |
| gammafsreg |
Variable selection in generalised linear regression models with forward selection |
| gammafsreg_2 |
Internal MXM Functions |
| gee.ci.mm |
Symmetric conditional independence test with clustered data |
| gee.condregs |
Conditional independence regression based tests |
| gee.mmhc.skel |
The skeleton of a Bayesian network as produced by MMHC |
| gee.pc.skel |
The skeleton of a Bayesian network produced by the PC algorithm |
| gee.univregs |
Univariate regression based tests |
| generatefolds |
Generate random folds for cross-validation |
| glm.bsreg |
Variable selection in generalised linear regression models with backward selection |
| glm.bsreg2 |
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 |
| glmm.bsreg |
Backward selection regression for GLMM |
| glmm.ci.mm |
Symmetric conditional independence test with clustered data |
| glmm.condregs |
Conditional independence regression based tests |
| glmm.cr.bsreg |
Internal MXM Functions |
| glmm.mmhc.skel |
The skeleton of a Bayesian network as produced by MMHC |
| glmm.nb.bsreg |
Internal MXM Functions |
| glmm.nb.reps.bsreg |
Internal MXM Functions |
| glmm.ordinal.bsreg |
Internal MXM Functions |
| glmm.ordinal.reps.bsreg |
Internal MXM Functions |
| glmm.pc.skel |
The skeleton of a Bayesian network produced by the PC algorithm |
| glmm.univregs |
Univariate regression based tests |
| gomp |
Generic orthogonal matching pursuit (gOMP) |
| gomp.path |
Generic orthogonal matching pursuit (gOMP) |
| gomp2 |
Internal MXM Functions |
| group.mvbetas |
Calculation of the constant and slope for each subject over time |
| gSquare |
G-square conditional independence test for discrete data |
| 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 |
| mae.mxm |
Cross-Validation for SES and MMPC |
| mammpc.output |
Class '"mammpc.output"' |
| mammpc.output-class |
Class '"mammpc.output"' |
| mammpc.output-method |
Class '"mammpc.output"' |
| mases.output |
Class '"mases.output"' |
| mases.output-class |
Class '"mases.output"' |
| mases.output-method |
Class '"mases.output"' |
| max_min_assoc |
Internal MXM Functions |
| max_min_assoc.gee |
Internal MXM Functions |
| max_min_assoc.glmm |
Internal MXM Functions |
| max_min_assoc.ma |
Internal MXM Functions |
| mb |
Returns the Markov blanket of a node (or variable) |
| mci.mxm |
Cross-Validation for SES and MMPC |
| min_assoc |
Internal MXM Functions |
| min_assoc.gee |
Internal MXM Functions |
| min_assoc.glmm |
Internal MXM Functions |
| min_assoc.ma |
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.gee |
SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data |
| mmpc.gee.model |
Generalised linear mixed model(s) based obtained from glmm SES or MMPC |
| MMPC.gee.output |
Class '"MMPC.gee.output"' |
| MMPC.gee.output-class |
Class '"MMPC.gee.output"' |
| MMPC.gee.output-method |
Class '"MMPC.gee.output"' |
| mmpc.gee2 |
mmpc.glmm2/mmpc.gee2: Fast Feature selection algorithm for identifying minimal feature subsets with correlated data |
| MMPC.glmm |
SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data |
| mmpc.glmm.model |
Generalised linear mixed model(s) based obtained from glmm SES or MMPC |
| MMPC.glmm.output |
Class '"MMPC.glmm.output"' |
| MMPC.glmm.output-class |
Class '"MMPC.glmm.output"' |
| MMPC.glmm.output-method |
Class '"MMPC.glmm.output"' |
| mmpc.glmm2 |
mmpc.glmm2/mmpc.gee2: Fast Feature selection algorithm for identifying minimal feature subsets with correlated data |
| mmpc.model |
Regression model(s) obtained from SES or MMPC |
| mmpc.or |
Bayesian Network construction using a hybrid of MMPC and PC |
| mmpc.path |
MMPC solution paths for many combinations of hyper-parameters |
| MMPC.timeclass |
Feature selection using SES and MMPC for classifiication with longitudinal data |
| mmpc.timeclass.model |
Regression model(s) obtained from SES.timeclass or MMPC.timeclass |
| mmpc2 |
A fast version of MMPC |
| mmpcbackphase |
Backward phase of MMPC |
| MMPCoutput |
Class '"MMPCoutput"' |
| MMPCoutput-class |
Class '"MMPCoutput"' |
| MMPCoutput-method |
Class '"MMPCoutput"' |
| modeler |
Generic regression modelling function |
| mse.mxm |
Cross-Validation for SES and MMPC |
| multinom.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.sel |
Variable selection using the PC-simple algorithm |
| pc.skel |
The skeleton of a Bayesian network produced by the PC algorithm |
| pc.skel.boot |
The skeleton of a Bayesian network produced by the PC algorithm |
| pearson_condis |
Internal MXM Functions |
| pearson_condis.rob |
Internal MXM Functions |
| perm.apply_ideq |
Internal MXM Functions |
| perm.betaregs |
Many simple beta regressions. |
| perm.IdentifyEquivalence |
Internal MXM Functions |
| perm.identifyTheEquivalent |
Internal MXM Functions |
| perm.Internalmmpc |
Internal MXM Functions |
| perm.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 |
| perm.mmpc.path |
MMPC solution paths for many combinations of hyper-parameters |
| perm.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 |
| perm.univariateScore |
Internal MXM Functions |
| perm.univregs |
Univariate regression based tests |
| perm.zipregs |
Many simple zero inflated Poisson regressions. |
| permBeta |
Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors |
| permBinom |
Binomial regression conditional independence test for success rates (binomial) |
| permClogit |
Conditional independence test based on conditional logistic regression for case control studies |
| permcor |
Permutation based p-value for the Pearson correlation coefficient |
| permcorrels |
Permutation based p-value for the Pearson correlation coefficient |
| permCR |
Conditional independence test for survival data |
| permDcor |
Fisher and Spearman conditional independence test for continuous class variables |
| permER |
Conditional independence test for survival data |
| permFisher |
Fisher and Spearman conditional independence test for continuous class variables |
| permGamma |
Regression conditional independence test for positive response variables. |
| permgSquare |
G-square conditional independence test for discrete data |
| permIGreg |
Regression conditional independence test for positive response variables. |
| permLLR |
Conditional independence test for survival data |
| permLogistic |
Conditional independence test for binary, categorical or ordinal class variables |
| permMMFisher |
Fisher and Spearman conditional independence test for continuous class variables |
| permMMReg |
Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| permMultinom |
Conditional independence test for binary, categorical or ordinal class variables |
| permMVreg |
Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| permNB |
Regression conditional independence test for discrete (counts) class dependent variables |
| permNormLog |
Regression conditional independence test for positive response variables. |
| permOrdinal |
Conditional independence test for binary, categorical or ordinal class variables |
| permPois |
Regression conditional independence test for discrete (counts) class dependent variables |
| permReg |
Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| permRQ |
Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| permTobit |
Conditional independence test for survival data |
| permWR |
Conditional independence test for survival data |
| permZIP |
Regression conditional independence test for discrete (counts) class dependent variables |
| pi0est |
Estimation of the percentage of Null p-values |
| plot-method |
Class '"MMPC.gee.output"' |
| plot-method |
Class '"MMPC.glmm.output"' |
| plot-method |
Class '"MMPCoutput"' |
| plot-method |
Class '"SES.gee.output"' |
| plot-method |
Class '"SES.glmm.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 |
| pval.mixbeta |
Fit a mixture of beta distributions in p-values |
| pve.mxm |
Cross-Validation for SES and MMPC |
| score.univregs |
Univariate regression based tests |
| 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.gee |
SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data |
| ses.gee.model |
Generalised linear mixed model(s) based obtained from glmm SES or MMPC |
| SES.gee.output |
Class '"SES.gee.output"' |
| SES.gee.output-class |
Class '"SES.gee.output"' |
| SES.gee.output-method |
Class '"SES.gee.output"' |
| SES.glmm |
SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data |
| ses.glmm.model |
Generalised linear mixed model(s) based obtained from glmm SES or MMPC |
| SES.glmm.output |
Class '"SES.glmm.output"' |
| SES.glmm.output-class |
Class '"SES.glmm.output"' |
| SES.glmm.output-method |
Class '"SES.glmm.output"' |
| ses.model |
Regression model(s) obtained from SES or MMPC |
| SES.timeclass |
Feature selection using SES and MMPC for classifiication with longitudinal data |
| ses.timeclass.model |
Regression model(s) obtained from SES.timeclass or MMPC.timeclass |
| SESoutput |
Class '"SESoutput"' |
| SESoutput-class |
Class '"SESoutput"' |
| SESoutput-method |
Class '"SESoutput"' |
| shd |
Structural Hamming distance between two partially oriented DAGs |
| sp.logiregs |
Many approximate simple logistic regressions. |
| spml.bsreg |
Internal MXM Functions |
| supervised.pca |
Supervised PCA |
| tc.plot |
Plot of longitudinal data |
| test.maker |
Internal MXM Functions |
| 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 |
| testIndGamma |
Regression conditional independence test for positive response variables. |
| testIndGEEGamma |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGEELogistic |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGEENormLog |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGEEPois |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGEEReg |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGLMMCR |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGLMMGamma |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGLMMLogistic |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGLMMNB |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGLMMNormLog |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGLMMOrdinal |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGLMMPois |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndGLMMReg |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndIGreg |
Regression conditional independence test for positive response variables. |
| testIndLMM |
Linear mixed models conditional independence test for longitudinal class variables |
| testIndLogistic |
Conditional independence test for binary, categorical or ordinal class variables |
| testIndMMFisher |
Fisher and Spearman conditional independence test for continuous class variables |
| testIndMMReg |
Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| testIndMultinom |
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 |
| testIndNormLog |
Regression conditional independence test for positive response variables. |
| testIndOrdinal |
Conditional independence test for binary, categorical or ordinal class variables |
| testIndPois |
Regression conditional independence test for discrete (counts) class dependent variables |
| testIndQBinom |
Conditional independence test for binary, categorical or ordinal class variables |
| testIndQPois |
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 |
| testIndSPML |
Circular regression conditional independence test for circular class dependent variables and continuous predictors. |
| testIndTimeLogistic |
Conditional independence test for the static-longitudinal scenario |
| testIndTimeMultinom |
Conditional independence test for the static-longitudinal scenario |
| testIndTobit |
Conditional independence test for survival data |
| testIndZIP |
Regression conditional independence test for discrete (counts) class dependent variables |
| topological_sort |
Topological sort of a DAG |
| transitiveClosure |
Returns the transitive closure of an adjacency matrix |
| triangles.search |
Search for triangles in an undirected graph |
| wald.betaregs |
Many simple beta regressions. |
| wald.Internalmmpc |
Internal MXM Functions |
| wald.Internalses |
Internal MXM Functions |
| wald.logisticregs |
Many Wald based tests for logistic and Poisson regressions with continuous predictors |
| wald.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 |
| wald.mmpc.path |
MMPC solution paths for many combinations of hyper-parameters |
| wald.poissonregs |
Many Wald based tests for logistic and Poisson regressions with continuous predictors |
| wald.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 |
| wald.univariateScore |
Internal MXM Functions |
| wald.univregs |
Univariate regression based tests |
| wald.zipregs |
Many simple zero inflated Poisson regressions. |
| waldBeta |
Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors |
| waldBinom |
Binomial regression conditional independence test for success rates (binomial) |
| waldCR |
Conditional independence test for survival data |
| waldER |
Conditional independence test for survival data |
| waldGamma |
Regression conditional independence test for positive response variables. |
| waldIGreg |
Regression conditional independence test for positive response variables. |
| waldLLR |
Conditional independence test for survival data |
| waldLogistic |
Conditional independence test for binary, categorical or ordinal class variables |
| waldmmpc.model |
Regression model(s) obtained from SES or MMPC |
| waldMMReg |
Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
| waldNB |
Regression conditional independence test for discrete (counts) class dependent variables |
| waldNormLog |
Regression conditional independence test for positive response variables. |
| waldOrdinal |
Conditional independence test for binary, categorical or ordinal class variables |
| waldPois |
Regression conditional independence test for discrete (counts) class dependent variables |
| waldQBinom |
Conditional independence test for binary, categorical or ordinal class variables |
| waldses.model |
Regression model(s) obtained from SES or MMPC |
| waldTobit |
Conditional independence test for survival data |
| waldWR |
Conditional independence test for survival data |
| waldZIP |
Regression conditional independence test for discrete (counts) class dependent variables |
| weibreg.mxm |
Cross-Validation for SES and MMPC |
| wr.fsreg |
Internal MXM Functions |
| wr.fsreg_2 |
Internal MXM Functions |