The goal of 'tidyplate' is to help researchers convert
    different types of microplates into tibbles which can be used in data
    analysis. It accepts xlsx and csv files formatted in a specific way as
    input. It supports all types of standard microplate formats such as
    6-well, 12-well, 24-well, 48-well, 96-well, 384-well, and, 1536-well
    plates.

The goal of 'tidyplots' is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.

Provides functions such as str_crush(), add_missing_column(), 
  coalesce_data() and drop_na_all() that complement 'tidyverse' functionality
  or functions that provide alternative behaviors such as if_else2()
  and str_detect2().

Parse XML documents from the Open Access subset of Europe PubMed Central <https://europepmc.org>
    including section paragraphs, tables, captions and references.

We provide a tidy grammar of population genetics, facilitating 
    the manipulation and analysis of data on biallelic single nucleotide
    polymorphisms (SNPs). 'tidypopgen' scales to very large genetic datasets
    by storing genotypes on disk, and performing operations on them in 
    chunks, without ever loading all data in memory. The full
    functionalities of the package are described in Carter et al. (2025)
    <doi:10.1101/2025.06.06.658325>.

Bayesian analysis used here to answer the question: "when
    looking at resampling results, are the differences between models
    'real'?" To answer this, a model can be created were the performance
    statistic is the resampling statistics (e.g. accuracy or RMSE). These
    values are explained by the model types. In doing this, we can get
    parameter estimates for each model's affect on performance and make
    statistical (and practical) comparisons between models. The methods
    included here are similar to Benavoli et al (2017)
    <https://jmlr.org/papers/v18/16-305.html>.