Several implementations of a novel Bayesian hierarchical statistical model of nucleotide
	recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.)
	for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) <doi:10.1261/rna.079451.122>).
	NR-seq is a powerful extension of RNA-seq that provides information about the kinetics
	of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking
	in standard RNA-seq data. The statistical model makes maximal use of these high-throughput
	datasets by sharing information across transcripts to significantly improve
	uncertainty quantification and increase statistical power. 'bakR' includes a maximally
	efficient implementation of this model for conservative initial investigations of datasets. 'bakR'
	also provides more highly powered implementations using the probabilistic programming language
	'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test
	adjusted statistical inference with the output of these model implementations to
	help biologists separate signal from background. Methods to automatically visualize key
	results and detect batch effects are also provided.

Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.

Select balanced and spatially balanced probability samples in multi-dimensional spaces
    with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of
    the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012)
    <doi:10.1111/j.1541-0420.2011.01699.x> and spatially correlated Poisson sampling by Grafström (2012)
    <doi:10.1016/j.jspi.2011.07.003> are included. Also the cube method (for balanced sampling) and
    the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013)
    <doi:10.1002/env.2194>.

Statistical decision in proteomics data using a hierarchical
    Bayesian model. There are two regression models for describing the 
    mean-variance trend, a gamma regression or a latent gamma mixture
    regression. The regression model is then used as an Empirical Bayes
    estimator for the prior on the variance in a peptide. Further, it assumes
    that each measurement has an uncertainty (increased variance) associated
    with it that is also inferred. Finally, it tries to estimate the posterior
    distribution (by Hamiltonian Monte Carlo) for the differences in means for
    each peptide in the data. Once the posterior is inferred, it integrates the
    tails to estimate the probability of error from which a statistical decision
    can be made.
    See Berg and Popescu for details (<doi:10.1016/j.mcpro.2023.100658>).