A complete rewrite and reimagining of 'bakR' (see 'Vock et al.' (2025) <doi:10.1371/journal.pcbi.1013179>).
  Designed to support a wide array of analyses of nucleotide recoding RNA-seq (NR-seq) datasets of any
	type, including TimeLapse-seq/SLAM-seq/TUC-seq, Start-TimeLapse-seq (STL-seq),
	TT-TimeLapse-seq (TT-TL-seq), and subcellular NR-seq. 'EZbakR' extends
	standard NR-seq standard NR-seq mutational modeling to support multi-label analyses
	(e.g., 4sU and 6sG dual labeling), and implements an improved hierarchical model to better
	account for transcript-to-transcript variance in metabolic label incorporation.
	'EZbakR' also generalized dynamical systems modeling of NR-seq data to support analyses
	of premature mRNA processing and flow between subcellular compartments. Finally,
	'EZbakR' implements flexible and well-powered comparative analyses of all
	estimated parameters via design matrix-specified generalized linear modeling.

A tool to operate a batch of univariate or multivariate Cox
    models and return tidy result.

Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 <doi:10.1080/10705519909540118>) with the same parameters (population model, number of observations, etc.) as the model under consideration.

Because fungicide resistance is an important phenotypic trait for
    fungi and oomycetes, it is necessary to have a standardized method of
    statistically analyzing the Effective Concentration (EC) values. This
    package is designed for those who are not terribly familiar with R to be
    able to analyze and plot an entire set of isolates using the 'drc' package.

Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at <doi:10.48550/arXiv.2409.18227>.