Tidying Methods for Mixed Models

Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.

A Tidy Wrapper Around 'gtrendsR'

Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package. Use four spaces when indenting paragraphs within the Description.

Tidy Characterizations of Model Performance

Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).

Tidy Data Validation Reports

Tools for creating data validation pipelines and tidy reports. This package offers a framework for exploring and validating data frame like objects using 'dplyr' grammar of data manipulation.

Heatmaps from Tidy Data

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.

Easily Tidy Gapminder Datasets

A toolset that allows you to easily import and tidy data sheets retrieved from Gapminder data web tools. It will therefore contribute to reduce the time used in data cleaning of Gapminder indicator data sheets as they are very messy.

Tidy Modelling for Nested Data

A modelling framework for nested data using the 'tidymodels' ecosystem. Specify how to nest data using the 'recipes' package, create testing and training splits using 'rsample', and fit models to this data using the 'parsnip' and 'workflows' packages. Allows any model to be fit to nested data.

Tidy Presentation of Clinical Reporting

Streamlined statistical reporting in 'Rmarkdown' environments. Facilitates the automated reporting of descriptive statistics, multiple univariate models, multivariable models and tables combining these outputs. Plotting functions include customisable survival curves, forest plots from logistic and ordinal regression and bivariate comparison plots.

Tidy Tools for 'Raster' Data

Facilities to work with vector and raster data in efficient repeatable and systematic work flow. Missing functionality in existing packages is included here to allow extraction from raster data with 'simple features' and 'Spatial' types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use.

Tidy and Geospatial Kernel Smoothing

Extensions of the kernel smoothing functions from the 'ks' package for compatibility with the tidyverse and geospatial ecosystems .