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.

Enables the creation of 'Moodle' quiz questions using literate
  programming with R Markdown. This makes it easy to quickly create a quiz that
  can be randomly replicated with new datasets, questions, and options for 
  answers.

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. (2025)
  <doi:10.32614/RJ-2025-031>.

Tools for predicting moonlight intensity on the ground based on
    the position of the moon, atmospheric conditions, and other factors.
    Provides functions to calculate moonlight intensity and related statistics
    for ecological and behavioral research, offering more accurate estimates
    than simple moon phase calculations. The underlying model is described in
    Smielak (2023) <doi:10.1007/s00265-022-03287-2>.

Predicts ground-level illuminance from moonlight, sunlight, and twilight for specified locations and time periods. The package is intended for field studies in ecology and behavior where natural light levels are used as predictor variables. See Poon et al. (2024) <doi:10.1111/2041-210X.14299>. Calculations use astronomical quantities from 'suncalc' and published illuminance models, including Austin et al. (1976) <doi:10.2307/2402251> and Seidelmann (1992) <ISBN:0935702687>.