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>.

Detection of runs of homozygosity and of heterozygosity
    in diploid genomes using two methods: sliding windows (Purcell et al (2007)
    <doi:10.1086/519795>) and consecutive runs (Marras et al (2015)
    <doi:10.1111/age.12259>).

Provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007, <https://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a>) using 'ROI' <https://cran.r-project.org/package=ROI> or 'lpSolveAPI' <https://cran.r-project.org/package=lpSolveAPI>. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the 'brglm2' <https://cran.r-project.org/package=brglm2> package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration. According to the results in Lesaffre & Albert (1989, <https://www.jstor.org/stable/2345845>), divergence of those ratios indicates data separation.