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didec (version 1.1.0)

Directed Dependence Coefficient

Description

Directed Dependence Coefficient (didec) is a measure of functional dependence. Multivariate Feature Ordering by Conditional Independence (MFOCI) is a variable selection algorithm based on didec. Hierarchical Variable Clustering (VarClustPartition) is a variable clustering method based on didec. For more information, see the paper by Ansari and Fuchs (2025, ), and the paper by Fuchs and Wang (2024, ).

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Version

Install

install.packages('didec')

Monthly Downloads

148

Version

1.1.0

License

MIT + file LICENSE

Maintainer

Yuping Wang

Last Published

February 2nd, 2026

Functions in didec (1.1.0)

plot_dendrogram

plotting a dendrogram with colored branches

Powerset without empty set

Split of two classes of variables based on different distance function

Multivariate feature ordering by conditional independence.

Estimation for multivariate concordance measures

Read a dendrogram from a list for hierarchical clustering result

plot_Adiam.Msplit

Plot the trade-off of Adiam and Msplit

Distance Matrix Computation using distance function based on T^q

dist.mat.concor

Distance Matrix Computation using distance function based on multivariate concordance measures

Estimate for T(Y,X) based on function codec

Computes the directed dependence coefficient.

VarClustPartition

Hierarchical variable clustering and partition.

Average diameter & Maximum split of every partition of a given dendrogram

Markov product estimate from single (q=1) endogenuous and (p>=1) exogenous variables based on dimension reduction

Estimate for \(\xi\)(Y,X) using codec function

Estimate for T_bar(Y,X) based on dimension reduction principle

Diameter of a class of variables based on different distance function

Estimate for \(\xi\)(Y,X) based on dimension reduction principle

Estimate for T_bar(Y,X) based on function Codec & a sample of all / all increasing / all decreasing permutations

Silhouette value for the i. variable given variable partition

Silhouette.coefficient

Silhouette coefficients given a dendrogram

Bioclimatic variables

Clustering a set of variables using distance function based on multivariate concordance measures

Cluster a set of variables using distance function based on predictive measure

Estimate for T(Y,X) based on dimension reduction principle

distance function based on multivariate concordance measures

plot_Silhouette.coefficient

Plot the Silhouette coefficient

distance function based on T