Provides a set of functions with a common framework for age-depth model management, 
  stratigraphic visualization, and common statistical transformations. The focus of the
  package is stratigraphic visualization, for which 'ggplot2' components are provided
  to reproduce the scales, geometries, facets, and theme elements commonly used in
  publication-quality stratigraphic diagrams. Helpers are also provided to reproduce
  the exploratory statistical summaries that are frequently included on
  stratigraphic diagrams. See Dunnington et al. (2021) <doi:10.18637/jss.v101.i07>.

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.

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.

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>.

It parses a fitted 'R' model object, and returns a formula in
    'Tidy Eval' code that calculates the predictions.  It works with
    several databases back-ends because it leverages 'dplyr' and 'dbplyr'
    for the final 'SQL' translation of the algorithm. It currently
    supports lm(), glm(), randomForest(), ranger(), earth(),
    xgb.Booster.complete(), cubist(), and ctree() models.