Implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with 'Rcpp' for efficiency and 'R6' for flexible, object-oriented design, the package provides a unified framework for performing or creating custom permutation tests.

Principal component analysis (PCA) is one of the most widely used data analysis techniques.  This package provides a series of vignettes explaining PCA starting from basic concepts. The primary purpose is to serve as a self-study resource for anyone wishing to understand PCA better. A few convenience functions are provided as well.

Conducts various numerical analyses and simulations in population genetics and evolutionary theory, primarily for the purpose of teaching (and learning about) key concepts in population & quantitative genetics, and evolutionary theory.

Create interactive tutorials using R Markdown. Use a
    combination of narrative, figures, videos, exercises, and quizzes to
    create self-paced tutorials for learning about R and R packages.

Data, scripts and code from chunks used as examples in the book 
   "Learn R: As a Language" 1ed and 2ed by Pedro J. Aphalo.
   ISBN 9780367182533 (pbk 1ed); ISBN 9780367182557 (hbk 1ed); ISBN 9780429060342 (ebk 1ed).

Supervised classification methods, which (if asked) can provide
    step-by-step explanations of the algorithms used, as described in
    PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to
    test them on, which highlight the strengths and weaknesses of each technique.

Summarizes characteristics of linear mixed effects models without 
    data or a fitted model by converting code for fitting lmer() from 'lme4' 
    and lme() from 'nlme' into tables, equations, and visuals. Outputs can be 
    used to learn how to fit linear mixed effects models in 'R' and to 
    communicate about these models in presentations, manuscripts, and analysis 
    plans.