Provides simplified access to the data from the Catalog of Theses and Dissertations 
    of the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES, <https://catalogodeteses.capes.gov.br>) 
    for the years 1987 through 2022. The dataset includes variables such as Higher Education 
    Institution (institution), Area of Concentration (area), Graduate Program Name (program_name), 
    Type of Work (type), Language of Work (language), Author Identification (author), Abstract 
    (abstract), Advisor Identification (advisor), Development Region (region), State (state).

Provides Capital Budgeting Analysis functionality and the essential Annuity loan functions.
    Also computes Loan Amortization Schedules including schedules with irregular payments.

A toolkit for computing and visualizing CAPL-2
    (Canadian Assessment of Physical Literacy, Second Edition;
    <https://www.capl-eclp.ca>) scores and interpretations from raw data.

Performs Correspondence Analysis on the given dataframe and plots the results in a scatterplot that emphasizes the geometric interpretation aspect of the analysis, following Borg-Groenen (2005) and Yelland (2010). It is particularly useful for highlighting the relationships between a selected row (or column) category and the column (or row) categories. See Borg-Groenen (2005, ISBN:978-0-387-28981-6); Yelland (2010) <doi:10.3888/tmj.12-4>.

Explore calcium (Ca) and phosphate (Pi) homeostasis with two novel 'Shiny' apps, 
    building upon on a previously published mathematical model written in C, 
    to ensure efficient computations. The underlying model is accessible
    here <https://pubmed.ncbi.nlm.nih.gov/28747359/)>.
    The first application explores the fundamentals of Ca-Pi homeostasis, 
    while the second provides interactive case studies for in-depth exploration of the topic, 
    thereby seeking to foster student engagement and an integrative understanding of Ca-Pi regulation.

Covariate Assisted Principal Regression (CAPR) for multiple 
    covariance-matrix outcomes. The method identifies (principal) projection 
    directions that maximize the log-likelihood of a log-linear regression model
    of the covariates. See Zhao et al. (2021), "Covariate Assisted Principal
    Regression for Covariance Matrix Outcomes" <doi:10.1093/biostatistics/kxz057>.