correspondence-analysis is a python module for simple correspondence analysis (CA) and multiple correspondence analysis (MCA).

A repository for "The Latent Semantic Space and Corresponding Brain Regions of the Functional Neuroimaging Literature" -- http://www.biorxiv.org/content/early/2017/07/20/157826

Python module for Factorial Analysis : Simple and Multiple Correspondence Analysis, Principal Components Analysis

Multivariate analysis (MVA) of high dimensional heterogeneous data

Efficient sparse matrix implementation for various "Principal Component Analysis"

Distributed words representations using Correspondence Analysis

This repository contains code for Clustering analysis and Correspondence analysis of online user reviews of Digital Camera . The reviews were collected using web scraping .

Correspondence Analysis of Comorbidity of Chronic Diseases in Brazil 2013

A script for automatic visualisation of Multiple Correspondence Analysis (MCA) results from FactoMineR in 3 dimensions using Plotly (exported as html)

This repository contains materials associated to the course "Multivariate Analysis" taught at the Faculty of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Interuniversity Program under the instructors "Ferran Revertar", "Miguel Salicru" and "Jan Graffelman"

ISSP 2012 data - correspondence analysis (tutkielman data-analyysiä)

Gradun luonnos

Very simple CA (correspondance analysis) implementation in R allowing to easily project supplementary rows afterwards

Machine Learning with KDB

Análise de Correspondência Simple utilizando dados da compra de Commodities do estado Maharashtra (índia) entre 2014 e 2016 no R Notebook.

Demonstrativo da análise não supervisionada de Correspondência Simples com por países e grau de letalidade da Covid-19.

Collaboration with NTHU Library

Análise de Correspondência Simples da distribuição de EPI's nos estados brasileiros pelo Ministério da Saúde.

Our analysis applies to the study of the results of 2017 French presidential elections according to each department and thus to study with R and Correspondence Analysis the behavior of the voters of each department.