compstatslib

This R Package is a collection of interactive tools and helper functions that help teachers and learners learn concepts in computational statistics. Useful for homework, in-class demonstrations, or self-learning.

Major Functions

Three types of functions are made available:

• Interactive functions let you use your mouse and/or keyboard to interact with a visualization of a technique (e.g., regression, PCA)
• Plot functions functions produce plots of data, distributions, or particular statistical concepts (often a non-interactive counterpart to an interactive function)
• Code functions are provided as examples of code that one might want to see (they are executable, but the major value is in seeing their code)

Statistical Tests

• interactive_t_test() Interactive visualization function that will show you a simulation of null and alternative distributions of the t-statistic. You will be able to play with the different parameters that affect hypothesis tests in order to see how their variation influences the null t and alternative t distributions, as well as statistical power.

Statistical Sampling

• interactive_sampling() Interactive sampling simulation that will sample given population data to show how a sampling statistic is distributed across repetitions of sampling exercise.
• plot_sample_ci() Simulated visualization of samples drawn from a given population function, with each sample’s confidence intervals displayed.

Linear Regression

• interactive_regression() Interactive visualization function that lets you point-and-click to add data points, while it automatically plots and updates a regression line and associated statistics.
• plot_regr() Plotting function that takes a dataframe of points (x, y) and plots them with a regression line and associated statistics.

Logistic Regression

• interactive_logit() Interactive visualization function that lets you point-and-click to add data points, while it automatically plots and updates a logistic regression line and associated statistics.
• plot_logit() Plotting function that takes a dataframe of points (x, y) and plots them with a logistic regression curve and associated statistics.

Principal Components Analysis

• interactive_pca() Interactive visualization function that lets you point-and-click to add data points, while it automatically plots and updates principal component vectors.

Precision

• machine_precision() Code function that shows how to find the smallest number your computer can effectively represent

Linear Algebra

• interactive_matrix_inverse() Interactive function that allows one to manipulate a matrix inversion.
• visualize_inverse() Plotting function that helps visual an inverse.

Installation

You can install the current development version from GitHub using the devtools package:

# install.packages("devtools")
devtools::install_github("soumyaray/compstatslib")

Feel free to send open issues or send pull requests. Happy hacking!