Generate motivational quotes and Shakespearean word combinations (bard–bits) that a user can consider for their personal projects. Each of the package functions takes two arguments, cat which default to any, and a a numeric or character seed to ensure reproducible results.

A function for plotting maps of agricultural field experiments that are laid out in grids.  See Ryder (1981) <doi:10.1017/S0014479700011601>.

Create a details HTML tag around R objects to place
    in a Markdown, 'Rmarkdown' and 'roxygen2' documentation.

Models for analyzing site occupancy and count data models
  with detection error, including single-visit based models,
  conditional distance sampling and time-removal models.
  Package development was supported by the
  Alberta Biodiversity Monitoring Institute
  and the Boreal Avian Modelling Project.

Non-normality could greatly distort the meta-analytic results, according to the simulation in Sun and Cheung (2020) <doi: 10.3758/s13428-019-01334-x>. This package aims to detect non-normality for two independent groups with only limited descriptive statistics, including mean, standard deviation, minimum, and maximum.

Allows users to quickly and easily detect data containing
    Personally Identifiable Information (PII) through convenience functions.

Researchers carried out a series of experiments passing a number 
  of essays to different GPT detection models. Juxtaposing detector predictions 
  for papers written by native and non-native English writers, the authors argue 
  that GPT detectors disproportionately classify real writing from non-native 
  English writers as AI-generated.

Time series analysis of network connectivity. Detects and visualizes change points between networks.
    Methods included in the package are discussed in depth in Baek, C., Gates, K. M., Leinwand, B., Pipiras, V. (2021)
    "Two sample tests for high-dimensional auto-covariances" <doi:10.1016/j.csda.2020.107067>
    and Baek, C., Gampe, M., Leinwand B., Lindquist K., Hopfinger J. and Gates K. (2023)
    “Detecting functional connectivity changes in fMRI data” <doi:10.1007/s11336-023-09908-7>.