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dad

The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The R package {dad} provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.

Rachid Boumaza, Pierre Santagostini, Smail Yousfi and Sabine Demotes-Mainard, https://journal.r-project.org/archive/2021/RJ-2021-071/index.html, The R Journal (2021) 13:2, pages 179--207, DOI: 10.32614/RJ-2021-071

Installation

Install the package from the CRAN:

install.packages("dad")

Or from the repository, using the devtools package:

install.packages("devtools")
devtools::install_git("https://forge.inrae.fr/dad/dad.git", build_vignettes = TRUE)

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Version

Install

install.packages('dad')

Monthly Downloads

285

Version

4.1.6

License

GPL (>= 2)

Maintainer

Pierre Santagostini

Last Published

June 10th, 2025

Functions in dad (4.1.6)

association measures

Association measures between several categorical variables of a data frame

Change numeric variables into factors

as.foldert.array

Data frame to foldert

Alsacian castles by year of building

Correlation matrices of a folder of data sets

as.foldert.data.frame

Data frame to foldert

bandwidth.parameter

Parameter of the normal reference rule

association measures for folder

Association measures between categorical variables of the data frames of a folder

castles.nondated

Non dated Alsacian castles

Dated Alsacian castles

In a folder: change numeric variables into factors

Distance between discrete probability distributions given the probabilities on their common support

Three-Way Data Analysis Through Densities

Distance between discrete probability distributions given the probabilities on their common support

Distance between discrete probability distributions given the probabilities on their common support

Distance between probability distributions of discrete variables given samples

Divergence between probability distributions of discrete variables given samples

Divergence between probability distributions of discrete variables given samples

Distance between probability distributions of discrete variables given samples

Divergence between discrete probability distributions given the probabilities on their common support

\(L^2\) distance between probability densities

Predicting the class of a group of individuals with discriminant analysis of probability distributions.

Distance between probability distributions of discrete variables given samples

Distance between discrete probability distributions given the probabilities on their common support

\(L^2\) distance between \(L^2\)-normed probability densities

Diploma x Socio professional group

discdd.misclass

Misclassification ratio in functional discriminant analysis of discrete probability distributions.

French departments and regions

\(L^2\) distance between \(L^2\)-normed Gaussian densities given their parameters

\(L^2\) distance between Gaussian densities given their parameters

Folder of data sets

Hierarchic cluster analysis of probability densities

Dual STATIS method (interstructure stage)

fdiscd.misclass

Misclassification ratio in functional discriminant analysis of probability densities.

Hierarchic folder of n data frames related in pairs by (n-1) keys

Predicting the class of a group of individuals with discriminant analysis of probability densities.

Multidimensional scaling of probability densities

Diploma x Socio professional group by departement in 2015

Functional PCA of probability densities among time

Select rows in all elements of a foldert

Folder of data sets among time

Select columns in all elements of a folder

Hellinger distance between Gaussian densities given their parameters

Hellinger distance between Gaussian densities

Functional PCA of probability densities

Select columns in all elements of a foldert

Hierarchic cluster analysis of discrete probability distributions

Select rows in all elements of a folder

Scores of fmdsd, dstatis, fpcad, or fpcat vs. moments, or scores of mdsdd vs. marginal distributions or association measures

interpret.fpcad

Scores of the fpcad function vs. moments of the densities

is.discdd.misclass

Class discdd.misclass

interpret.dstatis

Scores of the dstatis function vs. moments of the densities

interpret.fmdsd

Scores of the fmdsd function vs. moments of the densities

interpret.fpcat

Scores of the "fpcat" function vs. moments of the densities

interpret.mdsdd

Scores of the mdsdd function vs. marginal probability distributions or association measures

is.fdiscd.misclass

Class fdiscd.misclass

Class dstatis

is.discdd.predict

Class discdd.predict

Class foldert

is.fdiscd.predict

Class fdiscd.predict

Class fhclustd

Matrix of distances between discrete probability densities given samples

Jeffreys measure between Gaussian densities

matddchisqsympar

Matrix of distances between discrete probability densities given the probabilities on their common support

Jeffreys measure between Gaussian densities given their parameters

kurtosis.folder

Kurtosis coefficients of a folder of data sets

matddjeffreyspar

Matrix of divergences between discrete probability densities given the probabilities on their common support

Matrix of distances between discrete probability densities given samples

Class foldermtg

Matrix of distances between discrete probability densities given samples

Class folderh

mathellingerpar

Matrix of Hellinger distances between Gaussian densities given their parameters

matdistl2dnormpar

Matrix of \(L^2\) distances between \(L^2\)-normed Gaussian densities given their parameters

matddhellingerpar

Matrix of distances between discrete probability densities given the probabilities on their common support

Matrix of divergences between discrete probability densities given the probabilities on their common support

Matrix of \(L^2\) distances between Gaussian densities given their parameters

Matrix of Hellinger distances between Gaussian densities

Matrix of divergences between discrete probability densities given samples

Matrix of distances between discrete probability densities given the probabilities on their common support

Matrix of distances between discrete probability distributions given samples

\(L^2\) inner product of probability densities

\(L^2\) inner product of Gaussian densities given their parameters

Matrix of the Jeffreys measures (symmetrised Kullback-Leibler divergences) between Gaussian densities

Matrix of 2-Wassterstein distance between Gaussian densities

matwassersteinpar

Matrix of 2-Wasserstein distances between Gaussian densities

Matrix of \(L^2\) distances between \(L^2\)-normed probability densities

Matrix of \(L^2\) distances between probability densities

Matrix of \(L^2\) inner products of Gaussian densities

Matrix of \(L^2\) inner products of probability densities

Matrix of Jeffreys measures (symmetrised Kullback-Leibler divergences) between Gaussian densities

Plotting data of a foldert

Plotting scores of principal component analysis of density functions

Plotting scores of principal component analysis of density functions among time

Branching order of vertices

Components of upper scale of a vertex

Plotting a hierarchical clustering of discrete distributions

print.fdiscd.predict

Printing results of discriminant analysis of probability density functions

Means of a folder of data sets

Multidimensional scaling of discrete probability distributions

Plotting scores of multidimensional scaling of density functions

Plotting a hierarchical clustering

print.discdd.misclass

Printing results of discriminant analysis of discrete probability distributions

Class foldermtg

Plotting scores of multidimensional scaling analysis of discrete distributions

Class foldermtg

print.fdiscd.misclass

Printing results of discriminant analysis of probability density functions

print.discdd.predict

Printing results of discriminant analysis of discrete probability distributions

Plotting of two sets of variables

Printing results of STATIS method (interstructure) analysis

Remove rows in all elements of a folder

Remove rows in all elements of a foldert

Printing results of a hierarchical clustering of probability density functions

Printing results of a functional PCA of probability densities among time

Printing results of a hierarchical clustering of discrete distributions

Ranks of vertices in a decomposition

Printing results of a multidimensional scaling analysis of discrete distributions

Read a MTG (Multiscale Tree Graph) file

summary.foldermtg

Summary of an object of class foldermtg

Printing results of a multidimensional scaling analysis of probability densities

Remove columns in all elements of a folder

Summarize a folder

Remove cols in all elements of a foldert

Plotting scores of STATIS method (interstructure) analysis

Printing results of a functional PCA of probability densities

print.foldermtg

Printing an object of class foldermtg

skewness.folder

Skewness coefficients of a folder of data sets

Printing an object of class foldert

Square root of a symmetric, positive semi-definite matrix

Variance matrices of a folder of data sets

summary.folderh

Summarize a folderh

Rose variety leaves

2-Wasserstein distance between Gaussian densities

2-Wasserstein distance between Gaussian densities given their parameters

summary.foldert

Summarize a foldert

Rose leaf and internode dynamics

Coerce to a foldert

as.data.frame.folder

Folder to data frame

Coerce to a folderh

as.folderh.foldermtg

Build a hierarchic folder from an object of class foldermtg

as.folder.data.frame

Data frame to folder

as.data.frame.folderh

Hierarchic folder to data frame

appendtofolderh

Adds a data frame to a folderh.

as.data.frame.foldert

foldert to data frame

as.folder.folderh

Hierarchic folder to folder

Coerce to a folder