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Easily Install and Load the 'Tidyverse'
The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step. Learn more about the 'tidyverse' at < https://www.tidyverse.org>.
Lasso and Elastic-Net Regularized Generalized Linear Models
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see
Convert Country Names and Country Codes
Standardize country names, convert them into one of 40 different coding schemes, convert between coding schemes, and assign region descriptors.
Easy-to-Use Tools for Common Forms of Random Assignment and Sampling
Generates random assignments for common experimental designs and random samples for common sampling designs.
Tools for Multiple Imputation of Missing Data
Tools to perform analyses and combine results from multiple-imputation datasets.
Simple, Multiple and Joint Correspondence Analysis
Computation and visualization of simple, multiple and joint correspondence analysis.
Automatic Construction of Forced-Choice Tests
Forced-choice (FC) response has gained increasing popularity
and interest for its resistance to faking when well-designed (Cao &
Drasgow, 2019
Manage Environment Specific Configuration Values
Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value.
Extract and Visualize the Results of Multivariate Data Analyses
Provides some easy-to-use functions to extract and visualize the output of multivariate data analyses, including 'PCA' (Principal Component Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple Factor Analysis) functions from different R packages. It contains also functions for simplifying some clustering analysis steps and provides 'ggplot2' - based elegant data visualization.
Multivariate Exploratory Data Analysis and Data Mining
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).