Regularized Linear Modeling with Tidy Data

An extension to the 'R' tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.

Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

Simple Scraping and Tidy Webpage Summaries

Simple tools for scraping webpages, extracting common html tags and parsing contents to a tidy, tabular format. Tools help with extraction of page titles, links, images, rss feeds, social media handles and page metadata.

Extract and Tidy Canadian 'Hydrometric' Data

Provides functions to access historical and real-time national 'hydrometric' data from Water Survey of Canada data sources (< https://dd.weather.gc.ca/hydrometric/csv/> and < https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.

Download and Tidy IPC and CH Data

Utilities to access Integrated Food Security Phase Classification (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are available for all of the 'IPC-CH' Public API (< https://docs.api.ipcinfo.org>) simplified and advanced endpoints to easily download the data in a clean and tidy format.

A Tidy Framework for Changepoint Detection Analysis

Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages.

A Tidy Implementation of the Synthetic Control Method

A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.

Simple Conjoint Tidying, Analysis, and Visualization

Simple tidying, analysis, and visualization of conjoint (factorial) experiments, including estimation and visualization of average marginal component effects ('AMCEs') and marginal means ('MMs') for weighted and un-weighted survey data, along with useful reference category diagnostics and statistical tests. Estimation of 'AMCEs' is based upon methods described by Hainmueller, Hopkins, and Yamamoto (2014) .

A Tidy Interface to the 'Valhalla' Routing Engine

An interface to the 'Valhalla' routing engine’s application programming interfaces (APIs) for turn-by-turn routing, isochrones, and origin-destination analyses. Also includes several user-friendly functions for plotting outputs, and strives to follow "tidy" design principles. Please note that this package requires access to a running instance of 'Valhalla', which is open source and can be downloaded from < https://github.com/valhalla/valhalla>.

Tidy Estimation of Heterogeneous Treatment Effects

Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (n.d.) . 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.