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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.
Tidy Common R Statistical Functions
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)
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 (2023)
Access Tidy Education Finance Data
Provides easy access to tidy education finance data using Bellwether's methodology to combine NCES F-33 Survey, Census Bureau Small Area Income Poverty Estimates (SAIPE), and community data from the ACS 5-Year Estimates. The package simplifies downloading, caching, and filtering education finance data by year and state, enabling researchers and analysts to explore K-12 education funding patterns, revenue sources, expenditure categories, and demographic factors across U.S. school districts.
Tidy Dataframes and Expressions with Statistical Details
Utilities for producing dataframes with rich details for the
most common types of statistical approaches and tests: parametric,
nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation
analyses, contingency table analyses, and meta-analyses. The functions
are pipe-friendly and provide a consistent syntax to work with tidy
data. These dataframes additionally contain expressions with
statistical details, and can be used in graphing packages. This
package also forms the statistical processing backend for
'ggstatsplot'. References: Patil (2021)
Tidy Tools for Visualizing Mixture Models
The main function, plot_mm(), is used for (gg)plotting output from mixture models, including both densities and overlaying mixture weight component curves from the fit models in line with the tidy principles. The package includes several additional functions for added plot customization. Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each in active dev. Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, logistic regressions, linear regressions, and Poisson regressions.
Tidy Manipulation of Fourier Transformed Data
The 'fftab' package stores Fourier coefficients in a tibble and allows their manipulation in various ways. Functions are available for converting between complex, rectangular ('re', 'im'), and polar ('mod', 'arg') representations, as well as for extracting components as vectors or matrices. Inputs can include vectors, time series, and arrays of arbitrary dimensions, which are restored to their original form when inverting the transform. Since 'fftab' stores Fourier frequencies as columns in the tibble, many standard operations on spectral data can be easily performed using tidy packages like 'dplyr'.