The main objective of this package is to support the
    definition of 'Moodle' elements taking advantage of the power that R
    offers. In this first version, it allows the definition of quizzes
    to be included in the question bank.

Routines to generate fully randomized 'moodle' quizzes. It also contains 15 examples and a 'shiny' app.

A collection of functions to connect to a 'Moodle' database, cache relevant tables locally and generate learning analytics. 
    'Moodle' is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit <https://moodle.org>.

Several analysis-related functions for the book entitled "R
    statistics and graph for medical articles" (written in Korean), version 1,
    by Keon-Woong Moon with Korean demographic data with several plot
    functions.

Functions and examples based on the m-out-of-n bootstrap suggested by
  Politis, D.N. and Romano, J.P. (1994) <doi:10.1214/aos/1176325770>.
  Additionally there are functions to estimate the scaling factor tau
  and the subsampling size m. For a detailed description and a full list
  of references, see Dalitz, C. and Lögler, F. (2024)
  <doi:10.48550/arXiv.2412.05032>.

Visualization of multi-dimensional data arising in multi-objective optimization, including plots of the empirical attainment function (EAF), M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>, and symmetric Vorob'ev expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.

Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>.  It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.