r-cran-estimability

GNU R package providing tools for determining estimability of linear functions

The estimability package is a GNU R package providing tools for determining estimability of linear functions of regression coefficients, and 'epredict' methods that handle non-estimable cases correctly.

r-cran-fontawesome

GNU R support of 'Font Awesome' Icons

Easily and flexibly insert 'Font Awesome' icons into 'R Markdown' documents and 'Shiny' apps. These icons can be inserted into HTML content through inline 'SVG' tags or 'i' tags. There is also a utility function for exporting 'Font Awesome' icons as 'PNG' images for those situations where raster graphics are needed.

python3-plaster

Python multi-format configuration file loader interface - Python 3.x module

plaster is a loader interface around arbitrary config file formats. It exists to define a common API for applications to use when they wish to load configuration settings.

mariadb-backup

Backup tool for MariaDB server

Based on Xtrabackup, but improved to work with MariaDB server. This backup tool is guaranteed to be compatible with MariaDB server.

r-cran-paramhelpers

GNU R helpers for parameters in black-box optimization and tuning

Functions for parameter descriptions and operations in black-box optimization, tuning and machine learning. Parameters can be described (type, constraints, defaults, etc.), combined to parameter sets and can in general be programmed on. A useful OptPath object (archive) to log function evaluations is also provided.

r-cran-psych

GNU R procedures for psychological, psychometric, and personality research

This GNU R package provides a general purpose toolbox 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. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, 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.