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candisc:Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative.
Maintained by Michael Friendly. Last updated 1 days ago.
dimension-reductionmultivariate-linear-modelsvisualization
15 stars 8.99 score 221 scripts 3 dependents
strohne
volker:High-Level Functions for Tabulating, Charting and Reporting Survey Data
Craft polished tables and plots in Markdown reports. Simply choose whether to treat your data as counts or metrics, and the package will automatically generate well-designed default tables and plots for you. Boiled down to the basics, with labeling features and simple interactive reports. All functions are 'tidyverse' compatible.
Maintained by Jakob Jünger. Last updated 15 days ago.
5 stars 7.16 score 125 scripts
marekslenker
MorphoTools2:Multivariate Morphometric Analysis
Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Šlenker et al. 2022.
Maintained by Marek Šlenker. Last updated 6 months ago.
7 stars 5.02 score 9 scriptsfriendly
mvinfluence:Influence Measures and Diagnostic Plots for Multivariate Linear Models
Computes regression deletion diagnostics for multivariate linear models and provides some associated diagnostic plots. The diagnostic measures include hat-values (leverages), generalized Cook's distance, and generalized squared 'studentized' residuals. Several types of plots to detect influential observations are provided.
Maintained by Michael Friendly. Last updated 3 years ago.
multivariate-analysismultivariate-linear-regressionstatisticsvisualization
2 stars 4.41 score 26 scripts
auroreaa
ICSClust:Tandem Clustering with Invariant Coordinate Selection
Implementation of tandem clustering with invariant coordinate selection with different scatter matrices and several choices for the selection of components as described in Alfons, A., Archimbaud, A., Nordhausen, K.and Ruiz-Gazen, A. (2022) <arXiv:2212.06108>.
Maintained by Aurore Archimbaud. Last updated 2 years ago.
3.04 score 11 scripts
cran
MultivariateAnalysis:Pacote Para Analise Multivariada
Package with multivariate analysis methodologies for experiment evaluation. The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA, principal components, canonical variables, etc. (Pacote com metodologias de analise multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes principais, variaveis canonicas, etc.)
Maintained by Alcinei Mistico Azevedo. Last updated 12 months ago.
2.95 score
237triangle
SurveyCC:Canonical Correlation for Survey Data
Performs canonical correlation for survey data, including multiple tests of significance for secondary canonical correlations. A key feature of this package is that it incorporates survey data structure directly in a novel test of significance via a sequence of simple linear regression models on the canonical variates. See reference - Cruz-Cano, Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" The R Journal under review.
Maintained by Raul Cruz-Cano. Last updated 9 months ago.
2.00 score