R-universe search: exports:ellipses

Showing 7 of total 7 results (show query)

revelle

psych:Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.

Maintained by William Revelle. Last updated 7 days ago.

52 stars 14.12 score 29k scripts 317 dependents

muvisu

biplotEZ:EZ-to-Use Biplots

Provides users with an EZ-to-use platform for representing data with biplots. Currently principal component analysis (PCA), canonical variate analysis (CVA) and simple correspondence analysis (CA) biplots are included. This is accompanied by various formatting options for the samples and axes. Alpha-bags and concentration ellipses are included for visual enhancements and interpretation. For an extensive discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.

Maintained by Sugnet Lubbe. Last updated 18 days ago.

fortran

7 stars 8.39 score 30 scripts 1 dependents

cran

compositions:Compositional Data Analysis

Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.

Maintained by K. Gerald van den Boogaart. Last updated 1 years ago.

openblas

1 stars 6.35 score 36 dependents

stephenturner

Tmisc:Turner Miscellaneous

Miscellaneous utility functions for data manipulation, data tidying, and working with gene expression data and biological sequence data.

Maintained by Stephen Turner. Last updated 11 months ago.

2 stars 5.44 score 174 scripts 1 dependents

okgreece

Cluster.OBeu:Cluster Analysis 'OpenBudgets.eu'

Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface.

Maintained by Kleanthis Koupidis. Last updated 4 years ago.

cluster cluster-analysis clustering-algorithm clustering-measures estimate-clustering-parameters obeu open-budgets openbudgets

2 stars 4.75 score 14 scripts

rsoc

soc.ca:Specific Correspondence Analysis for the Social Sciences

Specific and class specific multiple correspondence analysis on survey-like data. Soc.ca is optimized to the needs of the social scientist and presents easily interpretable results in near publication ready quality.

Maintained by Anton Grau Larsen. Last updated 1 years ago.

14 stars 4.15 score 50 scripts

jeremyroos

gmgm:Gaussian Mixture Graphical Model Learning and Inference

Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

Maintained by Jérémy Roos. Last updated 3 years ago.

bayesian-networks gaussian-mixture-models inference machine-learning probabilistic-graphical-models

5 stars 3.40 score 7 scripts