Feature Selection (Including Multiple Solutions) and Bayesian Networks
ROC and AUC
Bootstrap bias correction for the performance of the cross-validation ...
Beta regression
Many simple beta regressions.
Variable selection in regression models with forward selection using B...
Variable selection in generalised linear models with forward selection...
Forward Backward Early Dropping selection regression for big data
Generic orthogonal matching pursuit(gOMP) for big data
Utilities for the skeleton of a (Bayesian) Network
Variable selection in regression models with backward selection
Conditional independence test for survival data
Certificate of exclusion from the selected variables set using SES or ...
Symmetric conditional independence test with mixed data
Conditional independence regression based tests
Conditional independence test for continuous class variables with and ...
Many conditional independence tests counting the number of times a pos...
Lower limit of the confidence of an edge
Drop all possible single terms from a model using the partial correlat...
Network construction using the partial correlation based forward regre...
Graph of unconditional associations
Cross-validation of the FBED with LMM
Cross-Validation for gOMP
Cross-Validation for SES and MMPC
Transforms a DAG into an essential graph
Backward selection regression using the eBIC
Backward selection regression for GLMM using the eBIC
eBIC for many regression models
Check Markov equivalence of two DAGs
Forward Backward Early Dropping selection regression with GEE
Forward Backward Early Dropping selection regression with GLMM
Forward Backward Early Dropping selection regression
Incremental BIC values and final regression model of the FBED algorith...
Returns and plots, if asked, the descendants or ancestors of one or al...
Variable selection in regression models with forward selection
Generate random folds for cross-validation
Variable selection in generalised linear regression models with backwa...
Variable selection in generalised linear regression models with forwar...
Backward selection regression for GLMM
Symmetric conditional independence test with clustered data
Generic orthogonal matching pursuit (gOMP)
Calculation of the constant and slope for each subject over time
G-square conditional independence test for discrete data
IAMB backward selection phase
IAMB variable selection
Total causal effect of a node on another node
Check whether a directed graph is acyclic
Variable selection in linear regression models with forward selection
Skeleton (local) around a node of the MMHC algorithm
Many simple quantile regressions using logistic regressions.
ma.ses: Feature selection algorithm for identifying multiple minimal, ...
Class "mammpc.output"
Class "mases.output"
Returns the Markov blanket of a node (or variable)
The skeleton of a Bayesian network as produced by MMHC
Max-min Markov blanket algorithm
Class "MMPC.gee.output"
Generalised linear mixed model(s) based obtained from glmm SES or MMPC
Class "MMPC.glmm.output"
mmpc.glmm2/mmpc.gee2: Fast Feature selection algorithm for identifying...
Bayesian Network construction using a hybrid of MMPC and PC
MMPC solution paths for many combinations of hyper-parameters
Regression model(s) obtained from SES.timeclass or MMPC.timeclass
A fast version of MMPC
Backward phase of MMPC
Class "MMPCoutput"
Generic regression modelling function
Internal MXM Functions
This is an R package that currently implements feature selection metho...
MXM Conditional independence tests
Returns the node(s) and their neighbour(s), if there are any.
Effective sample size for G^2 test in BNs with case control data
Probability residual of ordinal logistic regreession
Generalised ordinal regression
Partial correlation
The orientations part of the PC algorithm.
Variable selection using the PC-simple algorithm
The skeleton of a Bayesian network produced by the PC algorithm
Permutation based p-value for the Pearson correlation coefficient
Estimation of the percentage of Null p-values
Interactive plot of an (un)directed graph
Fit a mixture of beta distributions in p-values
Data simulation from a DAG.
Read big data or a big.matrix object
Regression modelling
Ridge regression
Ridge regression
Cross validation for the ridge regression
Class "SES.gee.output"
Class "SES.glmm.output"
SES.glmm/SES.gee: Feature selection algorithm for identifying multiple...
Regression model(s) obtained from SES or MMPC
SES: Feature selection algorithm for identifying multiple minimal, sta...
Feature selection using SES and MMPC for classifiication with longitud...
Class "SESoutput"
Structural Hamming distance between two partially oriented DAGs
Many approximate simple logistic regressions.
Supervised PCA
Plot of longitudinal data
Beta regression conditional independence test for proportions/percenta...
Binomial regression conditional independence test for success rates (b...
Conditional independence test based on conditional logistic regression...
Fisher and Spearman conditional independence test for continuous class...
Regression conditional independence test for positive response variabl...
Linear mixed models conditional independence test for longitudinal cla...
Linear mixed models conditional independence test for longitudinal cla...
Conditional independence test for binary, categorical or ordinal class...
Regression conditional independence test for discrete (counts) class d...
Linear (and non-linear) regression conditional independence test for c...
Circular regression conditional independence test for circular class d...
Conditional independence test for the static-longitudinal scenario
Conditional independence test for survival data
Topological sort of a DAG
Returns the transitive closure of an adjacency matrix
Search for triangles in an undirected graph
Undirected path(s) between two nodes
Univariate regression based tests
Many Wald based tests for logistic and Poisson regressions with contin...
Zero inflated Poisson and negative binomial regression
Many simple zero inflated Poisson regressions.
Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets. Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>. b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. <doi:10.1186/s12859-018-2023-7>. c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1): 19-30. <doi:10.1007/s41060-018-0097-y>. d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. <doi:10.1101/431734>. e) Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>. f) Tsagris, M. and Tsamardinos, I. (2019). Feature selection with the R package MXM. F1000Research 7: 1505. <doi:10.12688/f1000research.16216.2>. g) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39. h) The gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214-1224. <doi:10.1109/TCBB.2020.3029952>.