Tidy Interface to 'data.table'

A tidy interface to 'data.table', giving users the speed of 'data.table' while using tidyverse-like syntax.

Tidy Model Stacking

Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking.

A Tidy Tool for Phylogenetic Tree Data Manipulation

Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.

Tidy Quantitative Financial Analysis

Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples.

Tidy Output from Regular Expression Matching

Wrappers on 'regexpr' and 'gregexpr' to return the match results in tidy data frames.

Tidy Structural Equation Modeling

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.

Tidy Statistical Inference

The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.

MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format

Provides the 'Molecular Signatures Database' (MSigDB) gene sets typically used with the 'Gene Set Enrichment Analysis' (GSEA) software (Subramanian et al. 2005 , Liberzon et al. 2015 , Castanza et al. 2023 ) as an R data frame. The package includes the human genes as listed in MSigDB as well as the corresponding symbols and IDs for frequently studied model organisms such as mouse, rat, pig, fly, and yeast.

Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

Compute marginal effects and adjusted predictions from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Effects and predictions can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.

Tidy RSS for R

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.