Handle genomic data within data frames just as you would with 'GRanges'.
    This packages provides method to deal with genomic intervals the "tidy-way" which makes
    it simpler to integrate in the the general data munging process. The API is inspired by the
    popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package.

An intuitive interface for getting data from geocoding services.

In order to easily integrate geoRSS data into analysis,
    'tidygeoRSS' parses 'geo' feeds and returns 
    tidy simple features data frames.

A graph, while not "tidy" in itself, can be thought of as two
    tidy data frames describing node and edge data respectively.
    'tidygraph' provides an approach to manipulate these two virtual data
    frames using the API defined in the 'dplyr' package, as well as
    provides tidy interfaces to a lot of common graph algorithms.

This is a tidy implementation for heatmap.  At the
    moment it is based on the (great) package 'ComplexHeatmap'.  The goal
    of this package is to interface a tidy data frame with this powerful
    tool.  Some of the advantages are: Row and/or columns colour
    annotations are easy to integrate just specifying one parameter
    (column names).  Custom grouping of rows is easy to specify providing
    a grouped tbl. For example: df %>% group_by(...).  Labels size
    adjusted by row and column total number.  Default use of Brewer and
    Viridis palettes.

The goal of 'tidyheatmaps' is to simplify the generation of publication-ready heatmaps from tidy data. By offering an interface to the powerful 'pheatmap' package, it allows for the effortless creation of intricate heatmaps with minimal code.

Estimates heterogeneous treatment effects using tidy semantics 
    on experimental or observational data.  Methods are based on the doubly-robust
    learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple
    recipe for what machine learning algorithms to use in estimating the nuisance
    functions and 'tidyhte' will take care of cross-validation, estimation, model
    selection, diagnostics and construction of relevant quantities of interest about
    the variability of treatment effects.