glossary

There is a lot of necessary jargon to learn for coding. The goal of glossary is to provide a lightweight solution for making glossaries in educational materials written in quarto or R Markdown. This package provides functions to link terms in text to their definitions in an external glossary file, as well as create a glossary table of all linked terms at the end of a section.

Installation

You can (hopefully soon) install glossary from CRAN with:

install.packages("glossary")

You can install the development version of glossary from GitHub with:

# install.packages("devtools")
devtools::install_github("debruine/glossary")

Example

Click on the terms to see a popup definition.

library(glossary) 
glossary_path("inst/glossary.yml")
glossary_style("purple", "underline")

To calculate powerThe probability of rejecting the null hypothesis when it is false, for a specific analysis, effect size, sample size, and criteria for significance., you need to know the intended sample size, expected effect size‘quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest’ (Kelley & Preacher, 2012) (e.g., SESOISmallest Effect Size of Interest: the smallest effect that is theoretically or practically meaningful | See Equivalence Testing for Psychological Research for a tutorial on methods for choosing an SESOI.), and alphaThe threshold chosen in Neyman-Pearson hypothesis testing to distinguish test results that lead to the decision to reject the null hypothesis, or not, based on the desired upper bound of the Type 1 error rate. An alpha level of 5% is most commonly used, but other alpha levels can be used as long as they are determined and preregistered by the researcher before the data is analyzed. criterion.

See getting started for more details.