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====== SDA - software ====== | ====== SDA - software ====== | ||
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+ | ===== Packages in R ===== | ||
+ | prepared by **//Maria do Rosário de Oliveira Silva//** | ||
+ | |||
+ | A new package [[https://cran.r-project.org/web/packages/dataSDA/|dataSDA]] (June 2023) with SDA datasets appeared. | ||
+ | [[https://cran.r-project.org/web/packages/dataSDA/dataSDA.pdf|PDF]] | ||
+ | |||
+ | |||
+ | The total number of downloads done in 2022 for each package is obtained according to the same package. | ||
+ | |||
+ | - [[https://cran.r-project.org/web/packages/MAINT.Data/index.html|MAINT.Data]]: ''Model and Analyse Interval Data'' | ||
+ | * Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix. Parameters are estimated by maximum likelihood or robust trimmed maximum likelihood methods. In 2022 it registered **6361** downloads. | ||
+ | - [[https://cran.r-project.org/web/packages/RSDA/index.html|RSDA]]: ''R to Symbolic Data Analysis'' | ||
+ | *This package implements, in the symbolic case, certain techniques of automatic classification, as well as some linear models. In 2022 it registered ''5848'' downloads. | ||
+ | - [[https://cran.r-project.org/web/packages/HistDAWass/index.html|HistDAWass]]: ''Histogram-Valued Data Analysis'' | ||
+ | * The package contains unsupervised classification techniques, least squares regression, and tools for histogram-valued data and for histogram time series. The methods and the basic statistics are mainly based on the L<sub>2</sub> Wasserstein distance. In 2022 it registered **5124** downloads. | ||
+ | - [[https://cran.r-project.org/web/packages/mdsOpt/index.html|mdsOpt]]: ''Searching for Optimal MDS Procedure for Metric and Interval-Valued Data'' | ||
+ | * Select the optimal multidimensional scaling (MDS) procedure for metric and interval-valued data. This package also does conventional MDS. In 2022 it registered **4245** downloads. | ||
+ | - [[https://cran.r-project.org/web/packages/symbolicDA/index.html|symbolicDA]]: ''Analysis of Symbolic Data'' | ||
+ | * Implements several symbolic data analysis estimation and visualization methods, including MDS, clustering, decision trees, and kernel discriminant analysis, among others, for symbolic data. In 2022 it registered **3573** downloads. | ||
+ | - [[https://cran.r-project.org/web/packages/iRegression/index.html|iRegression]]: ''Regression Methods for Interval-Valued Variables'' | ||
+ | * Contains some important regression methods for interval-valued variables. In 2022 it registered **3130** downloads. | ||
+ | - [[https://cran.r-project.org/web/packages/GraphPCA/index.html|GraphPCA]]: ''Graphical Tools of Histogram PCA'' | ||
+ | * Extends PCA for histogram-valued data. In 2022 it registered **2661** downloads. | ||
+ | - [[https://cran.r-project.org/web/packages/ggESDA/index.html|ggESDA]]: ''Exploratory Symbolic Data Analysis with ggplot2'' | ||
+ | * Implements an extension of ''ggplot2'' and visualizes the symbolic data with multiple plots. In 2022 it registered **3287** downloads. | ||
+ | |||
+ | ===== Older software links ===== | ||
+ | |||
* [[http://www.info.fundp.ac.be/asso/SODAS2-software/|SODAS 2]]; [[http://www.ceremade.dauphine.fr/~touati/sodas-pagegarde.htm|@ ceremade.dauphine]] | * [[http://www.info.fundp.ac.be/asso/SODAS2-software/|SODAS 2]]; [[http://www.ceremade.dauphine.fr/~touati/sodas-pagegarde.htm|@ ceremade.dauphine]] | ||
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* [[http://cran.r-project.org/web/packages/MAINT.Data/index.html | MAINT.Data]] : Implements methodologies for modelling Interval Data, considering five different possible configurations structures for the variance-covariance matrix. It performs maximum likelihood estimation and statistical tests as well as (M)ANOVA and Linear and Quadratic Discriminant Analysis for all considered configurations. | * [[http://cran.r-project.org/web/packages/MAINT.Data/index.html | MAINT.Data]] : Implements methodologies for modelling Interval Data, considering five different possible configurations structures for the variance-covariance matrix. It performs maximum likelihood estimation and statistical tests as well as (M)ANOVA and Linear and Quadratic Discriminant Analysis for all considered configurations. | ||
+ | \\ \\ | ||
+ | [[:sda|Back to SDA]] |