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.

It creates an invisible layer that allow to see the 'Seurat' object 
    as tibble and interact seamlessly with the tidyverse.

Tidy standardized mean differences ('SMDs'). 'tidysmd' uses
    the 'smd' package to calculate standardized mean differences for
    variables in a data frame, returning the results in a tidy format.

Enables the analysis of spectroscopy data such as infrared ('IR'), Raman, and nuclear magnetic resonance ('NMR') using the tidy data framework from the 'tidyverse'. The 'tidyspec' package provides functions for data transformation, normalization, baseline correction, smoothing, derivatives, and both interactive and static visualization. It promotes structured, reproducible workflows for spectral data exploration and preprocessing. Implemented methods include Savitzky and Golay (1964) "Smoothing and Differentiation of Data by Simplified Least Squares Procedures" <doi:10.1021/ac60214a047>, Sternberg (1983) "Biomedical Image Processing" <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1654163>, Zimmermann and Kohler (1996) "Baseline correction using the rolling ball algorithm" <doi:10.1016/0168-583X(95)00908-6>, Beattie and Esmonde-White (2021) "Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra" <doi:10.1177/0003702820987847>, Wickham et al. (2019) "Welcome to the tidyverse" <doi:10.21105/joss.01686>, and Kuhn, Wickham and Hvitfeldt (2024) "recipes: Preprocessing and Feature Engineering Steps for Modeling" <https://CRAN.R-project.org/package=recipes>.