Description: Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017) <DOI:10.1201/b10345-2>.

Description: Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Title: Machine Learning with Higher-Order Factorization Machines
Description: Implementation of three machine learning approaches: Support Vector Machines (SVM) with a linear kernel, second-order Factorization Machines (FM), and higher-order Factorization Machines (HoFM).
Imports: Rcpp (>= 0.12.1), methods, Matrix
Packaged: 2016-12-01 21:23:31 UTC; Administrator

Packaged: 2017-03-20 23:41:01 UTC; Administrator

Packaged: 2017-08-08 11:19:12 UTC; Administrator

Description: Implementation of different machine learning approaches: Support Vector Machine with a linear kernel, second-order Factorization Machine, higher-order Factorization Machine, and adaptive-order Factorization Machine.
Packaged: 2017-10-11 12:57:18 UTC; Administrator

Title: Set of Assumptions for Factor and Principal Component Analysis
Authors@R: person("Jose", "Storopoli", role = c("aut", "cre"), email = "thestoropoli@gmail.com", comment = c(ORCID = "0000-0002-0559-5176"))
Description: Tests for Kaiser-Meyer-Olkin (KMO) and
communalities in a dataset. It provides a final sample by removing
variables in a iterable manner while keeping account of the variables
that were removed in each step. It follows the best practices and assumptions according to
Hair, Black, Babin & Anderson (2018, ISBN:9781473756540).
Packaged: 2020-02-29 15:32:34 UTC; storopoli
Author: Jose Storopoli [aut, cre] (<https://orcid.org/0000-0002-0559-5176>)