In ancient Chinese mythology, Bai Ze is a divine creature that knows the needs of everything.
    'baizer' provides data processing functions frequently used by the author.
    Hope this package also knows what you want!

Data about the bakers, challenges, and ratings 
    for "The Great British Bake Off", 
    from Wikipedia <https://en.wikipedia.org/wiki/The_Great_British_Bake_Off>.

Provides functions to specify, fit and visualize
    nested partially-latent class models (
    Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; 
    Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>;
    Wu and Chen (2021) <doi:10.1002/sim.8804>) for 
    inference of population disease etiology and individual diagnosis. In the motivating
    Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities
    of interest sum to one hundred percent, the PERCH scientists frequently refer to
    them as population etiology pie and individual etiology pie, hence the name of the package.

Easily launch, track, and control functions as
    background-parallel jobs. Includes robust utilities for job status,
    error handling, resource monitoring, and result collection. Designed
    for scalable workflows in interactive and automated settings (local or
    remote). Integrates with multiple backends; supports flexible
    automation pipelines and live job tracking. For more information, see
    <https://anirbanshaw24.github.io/bakerrr/>.

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.