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 |
censIndWR |
Conditional independence test for survival data |
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 |
comb_condis |
Internal MXM Functions |
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 |
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 |
corfs.network |
Network construction using the partial correlation based forward regression |
coxph.mxm |
Cross-Validation for SES and MMPC |
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 |
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 |
gammabsreg |
Variable selection in generalised linear regression models with backward selection |
gammafsreg |
Variable selection in generalised linear regression models with forward selection |
gammafsreg_2 |
Internal MXM Functions |
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 |
glmm.bsreg |
Backward selection regression for GLMM |
glmm.ci.mm |
Symmetric conditional independence test with clustered data |
glmm.pc.skel |
The skeleton of a Bayesian network produced by the PC algorithm |
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 |
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.or |
Bayesian Network construction using a hybrid of MMPC and PC |
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.model |
Generalised linear mixed model(s) based obtained from temporal SES or MMPC |
MMPC.temporal.output |
Class '"MMPC.temporal.output"' |
MMPC.temporal.output-class |
Class '"MMPC.temporal.output"' |
mmpcbackphase |
Backward phase of MMPC |
MMPCoutput |
Class '"MMPCoutput"' |
MMPCoutput-class |
Class '"MMPCoutput"' |
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.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.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. |
permLogistic |
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. |
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.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 |
pval.mixbeta |
Fit a mixture of beta distributions in p-values |
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.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"' |
shd |
Structural Hamming distance between two partially oriented DAGs |
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 |
testIndGamma |
Regression conditional independence test for positive response variables. |
testIndGLMM |
Linear mixed models conditional independence test for longitudinal class variables |
testIndIGreg |
Regression conditional independence test for positive 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 |
testIndNormLog |
Regression conditional independence test for positive response 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 |
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 |
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.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 |
waldBinary |
Conditional independence test for binary, categorical or ordinal class variables |
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. |
waldmmpc.model |
Regression model(s) obtained from SES or MMPC |
waldmmpc.path |
MMPC solution paths for many combinations of hyper-parameters |
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 |
waldses.model |
Regression model(s) obtained from SES or MMPC |
waldSpeedglm |
Conditional independence test for continuous, binary and discrete (counts) variables with thousands of observations |
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 |