My R Packages, Interactive Applications, and Repositories

Dr. rer. nat. Humberto LLinás Solano

Departamento de Matemáticas y Estadística, Universidad del Norte (Barranquilla, Colombia)

hllinas@uninorte.edu.co

02/10/26

Abstract

The theoretical background discussed above can be reviewed in my class notes, available in the following document: 2.2. Logistic Regression, or in the references by Llinás (2006), Llinás (2012), Llinás (2016), and Orozco (2020). Additional documents that may be of interest are available at RPubs::toc.

1 Preface

This document brings together a collection of R packages and interactive applications developed to support statistical analysis, teaching, and the exploration of computational and machine learning concepts.

The resources presented here cover several areas, including statistics and probability, logistic regression, topic modeling, optimization, and neural networks. Some of the tools are distributed as R packages, while others are interactive web applications designed to provide a more intuitive and visual understanding of selected concepts.

For each resource, links are provided to the corresponding package, application, documentation, or supporting materials. The collection is intended for students, researchers, and practitioners interested in both the theoretical and applied aspects of statistical and computational methods.

2 Statistical Package

2.0.1 `StatisticTeach1`

Version 0.1.2 — Interactive Tool for Statistics and Probability

StatisticTeach1 is an interactive R package designed to support the teaching and learning of fundamental concepts in statistics and probability.

View the package on CRAN.

library(StatisticTeach1)
runStatisticTeach1()

3 Logistic Regression Packages

3.0.1 `lsm`

Version 0.2.1.5 — Estimation of the Log-Likelihood of the Saturated Model in R

The lsm package provides tools for estimating the log-likelihood of the saturated model in the context of logistic regression.

View the documentation.

3.0.2 `glsm`

Version 0.0.0.6 — Saturated Model Log-Likelihood for Multinomial Outcomes in R

The glsm package extends saturated-model log-likelihood calculations to models involving multinomial outcomes.

View the documentation.

4 Topic Modeling and Rank-Frequency Analysis

4.0.1 `RIFanalysis`

Version 0.9.3 — Relative Importance Factor Analysis

RIFanalysis provides a workflow for the analysis, comparison, and ranking of relative importance in rank-frequency distributions using the Relative Importance Factor (RIF).

View the package on CRAN.

5 Interactive Applications

5.0.1 Interactive Gradient Descent in Two and Three Dimensions

This interactive application provides a visual exploration of the gradient descent algorithm in two- and three-dimensional settings, helping users understand the iterative optimization process and the effect of different configurations.

5.0.2 Neural Network Playground

The Neural Network Playground is an interactive application for exploring the structure and behavior of neural networks. It provides a visual environment for examining how network architecture, neurons, connections, and learning mechanisms contribute to the modeling process.

6 Repositories and Blogs

6.0.1 Statistical Timeline in R

Stats to AI Timeline

This GitHub repository presents a chronological overview of important developments in statistics, data science, machine learning, and artificial intelligence, with an emphasis on their historical and conceptual evolution.

View the repository on GitHub.

6.0.2 Global Scientific Production Template

Global Scientific Production Template

This GitHub repository provides a reusable template for analyzing and visualizing global scientific production. It is intended to support bibliometric and scientometric studies involving countries, institutions, publications, and other indicators of scientific activity.