dmi
(Package: mpmi) :
Calculate BCMI for categorical (discrete) data
This function calculates MI and BCMI between a set of discrete variables held as columns in a matrix. It also performs jackknife bias correction and provides a z-score for the hypothesis of no association. Also included are the *.pw functions that calculate MI between two vectors only. The *njk functions do not perform the jackknife and are therefore faster.
disc
(Package: mpmi) :
A group of simulated categorical (discrete) variables
50 observations on each of 75 categorical variables. These variables are designed to be similar to categorical single nucleotide polymorphism (SNP) data which have 3 categories (A, H and B where H represents a heterozygous mutation). There are no associations between any of the variables. The variables are stored as characters. See the vignette for details
cts
(Package: mpmi) :
A group of simulated continuous variables
50 observations on each of 100 variables. The data are simulated such that variables with similar indices are associated with the degree of association decaying as variables are further apart (i.e., a correlation or information matrix with have larger values near the diagonal). Details are given in the vignette.
mmi
(Package: mpmi) :
Calculate mixed-pair BCMI between a set of continuous variables and a set
This function calculates MI and BCMI between a set of continuous variables and a set of discrete variables (variables in columns). It also performs jackknife bias correction and provides a z-score for the hypothesis of no association. Also included are the *.pw functions that calculate MI between two vectors only. The *njk functions do not perform the jackknife and are therefore faster.
cmi
(Package: mpmi) :
Calculate BCMI between a set of continuous variables
This function calculates MI and BCMI between a set of continuous variables held as columns in a matrix. It also performs jackknife bias correction and provides a z-score for the hypothesis of no association. Also included are the *.pw functions that calculate MI between two vectors only. The *njk functions do not perform the jackknife and are therefore faster.
providing 
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