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>.

Builds both ROC (Receiver Operating Characteristic) and DET (Detection Error Tradeoff) curves from a set of predictors that are the
    results of a binary classification system. The curves give a general view of classifier performance
    and are useful for comparing different systems.

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 (Lele et al. 2012 <doi:10.1093/jpe/rtr042>, 
  Moreno et al. 2010 <doi:10.1890/09-1073.1>,
  Solymos et al. 2012 <doi:10.1002/env.1149>,
  Denes et al. 2016 <doi:10.1111/1365-2664.12818>),
  conditional distance sampling and time-removal models (QPAD)
  (Solymos et al. 2013 <doi:10.1111/2041-210X.12106>,
  Solymos et al. 2018 <doi:10.1650/CONDOR-18-32.1>),
  and single bin QPAD (SQPAD) models
  (Lele & Solymos 2025 <doi:10.1093/ornithapp/duaf078>).
  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.