With the objective of including data from RSS feeds into your analysis, 
    'tidyRSS' parses RSS, Atom and JSON feeds and returns a tidy data frame.

Provides functions to scale, log-transform and fit linear models within a 'tidyverse'-style R code framework.
    Intended to smooth over inconsistencies in output of base R statistical functions, allowing ease of teaching, learning and daily use. Inspired by the tidy principles used in 'broom' Robinson (2017) <doi:10.21105/joss.00341>.

Provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.

Fit species distribution models (SDMs) using the 'tidymodels' framework, 
  which provides a standardised interface to define models and process their 
  outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects,
  models and metrics specific to SDMs,
  as well as a number of specialised functions to process occurrences
  for contemporary and palaeo datasets. The full 
  functionalities of the package are described
  in Leonardi et al. (2024) <doi:10.1111/2041-210X.14406>.

A backend for the selecting functions of the 'tidyverse'.  It
    makes it easy to implement select-like functions in your own packages
    in a way that is consistent with other 'tidyverse' interfaces for
    selection.

A tidy workflow for generating, estimating, reporting,
    and plotting structural equation models using 'lavaan', 'OpenMx', or
    'Mplus'. Throughout this workflow, elements of syntax, results, and graphs
    are represented as 'tidy' data, making them easy to customize.
    Includes functionality to estimate latent class analyses, and to plot
    'dagitty' and 'igraph' objects.