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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://forgemia.inra.fr/dad/dad.git", build_vignettes = TRUE)

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Version

Install

install.packages('dad')

Monthly Downloads

429

Version

4.1.5

License

GPL (>= 2)

Maintainer

Pierre Santagostini

Last Published

November 22nd, 2024

Functions in dad (4.1.5)

association measures

Association measures between several categorical variables of a data frame

castles.nondated

Non dated Alsacian castles

as.foldert.data.frame

Data frame to foldert

association measures for folder

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

Divergence between probability distributions of discrete variables given samples

Divergence between probability distributions of discrete variables given samples

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

Distance between probability distributions of discrete variables given samples

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

Three-Way Data Analysis Through Densities

In a folder: change numeric variables into factors

Distance between probability distributions of discrete variables given samples

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

French departments and regions

\(L^2\) distance between probability densities

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

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

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

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

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

discdd.misclass

Misclassification ratio in functional discriminant analysis of discrete probability distributions.

Diploma x Socio professional group

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

Hierarchic cluster analysis of probability densities

fdiscd.misclass

Misclassification ratio in functional discriminant analysis of probability densities.

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

Diploma x Socio professional group by departement in 2015

Multidimensional scaling of probability densities

Dual STATIS method (interstructure stage)

Folder of data sets

Select columns in all elements of a folder

Select columns in all elements of a foldert

Functional PCA of probability densities among time

Hierarchic cluster analysis of discrete probability distributions

Select rows in all elements of a foldert

Select rows in all elements of a folder

Hellinger distance between Gaussian densities

Functional PCA of probability densities

Folder of data sets among time

Hellinger distance between Gaussian densities given their parameters

interpret.fpcat

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

is.discdd.misclass

Class discdd.misclass

interpret.dstatis

Scores of the dstatis function vs. moments of the densities

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

is.fdiscd.misclass

Class fdiscd.misclass

Class dstatis

is.fdiscd.predict

Class fdiscd.predict

interpret.mdsdd

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

is.discdd.predict

Class discdd.predict

interpret.fmdsd

Scores of the fmdsd function vs. moments of the densities

Class folderh

Class fhclustd

interpret.fpcad

Scores of the fpcad function vs. moments of the densities

Jeffreys measure between Gaussian densities

matddhellingerpar

Matrix of distances 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

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

Matrix of distances between discrete probability densities given samples

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

matddchisqsympar

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

Matrix of divergences between discrete probability densities given samples

matddjeffreyspar

Matrix of divergences 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

Means of a folder of data sets

Multidimensional scaling of discrete probability distributions

Matrix of distances between discrete probability distributions given samples

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

Branching order of vertices

Plotting scores of principal component analysis of density functions among time

Plotting a hierarchical clustering of discrete distributions

Components of upper scale of a vertex

Class foldermtg

Plotting scores of principal component analysis of density functions

matdistl2dnormpar

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

Class foldert

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

Plotting a hierarchical clustering

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

Class foldermtg

Plotting scores of multidimensional scaling analysis of discrete distributions

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

Printing results of a multidimensional scaling analysis of probability densities

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

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

Plotting scores of multidimensional scaling of density functions

print.foldermtg

Printing an object of class foldermtg

print.fdiscd.misclass

Printing results of discriminant analysis of probability density functions

matwassersteinpar

Matrix of 2-Wasserstein distances between Gaussian densities

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

Rose leaf and internode dynamics

Remove rows in all elements of a folder

Plotting of two sets of variables

Matrix of Hellinger distances between Gaussian densities

print.fdiscd.predict

Printing results of discriminant analysis of probability density functions

Remove rows in all elements of a foldert

summary.foldermtg

Summary of an object of class foldermtg

Printing results of STATIS method (interstructure) analysis

summary.foldert

Summarize a foldert

mathellingerpar

Matrix of Hellinger distances between Gaussian densities given their parameters

Summarize a folder

Ranks of vertices in a decomposition

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

Plotting scores of STATIS method (interstructure) analysis

Remove cols in all elements of a foldert

Remove columns in all elements of a folder

Variance matrices of a folder of data sets

summary.folderh

Summarize a folderh

Plotting data of a foldert

Rose variety leaves

Jeffreys measure between Gaussian densities given their parameters

Printing results of a multidimensional scaling analysis of discrete distributions

kurtosis.folder

Kurtosis coefficients of a folder of data sets

Read a MTG (Multiscale Tree Graph) file

skewness.folder

Skewness coefficients of a folder of data sets

Printing results of a hierarchical clustering of probability density functions

Square root of a symmetric, positive semi-definite matrix

Printing results of a hierarchical clustering of discrete distributions

Matrix of 2-Wassterstein distance between Gaussian densities

Printing results of a functional PCA of probability densities

2-Wasserstein distance between Gaussian densities

Printing an object of class foldert

print.discdd.misclass

Printing results of discriminant analysis of discrete probability distributions

print.discdd.predict

Printing results of discriminant analysis of discrete probability distributions

2-Wasserstein distance between Gaussian densities given their parameters

Printing results of a functional PCA of probability densities among time

as.data.frame.folderh

Hierarchic folder to data frame

as.data.frame.foldert

foldert to data frame

as.folderh.foldermtg

Build a hierarchic folder from an object of class foldermtg

Correlation matrices of a folder of data sets

Coerce to a foldert

Change numeric variables into factors

as.folder.data.frame

Data frame to folder

Coerce to a folder

as.folder.folderh

Hierarchic folder to folder

bandwidth.parameter

Parameter of the normal reference rule

as.data.frame.folder

Folder to data frame

as.foldert.array

Data frame to foldert

Coerce to a folderh

appendtofolderh

Adds a data frame to a folderh.

Dated Alsacian castles

Alsacian castles by year of building