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shinymrp: Applying Multilevel Regression and Poststratification in R shinymrp website

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shinymrp allows users to apply Multilevel Regression and Poststratification (MRP) methods to a variety of datasets, from electronic health records to sample survey data, through an end-to-end Bayesian data analysis workflow. Whether you’re a researcher, analyst, or data engineer, shinymrp provides robust tools for data cleaning, exploratory analysis, flexible model building, and insightful result visualization.

Getting Started

You can use shinymrp in two flexible ways:

Shiny App

The graphical user interface (GUI), built with the Shiny framework, is designed for newcomers and those looking for an interactive, code-free analysis experience.

Launch the app locally in R with:

shinymrp::run_app()

Try the Demo

Explore the Shiny app without installation via our online demo.

Need a walk-through? Watch our step-by-step video tutorial.

Object-Oriented Programming Interface

Leverage the full flexibility of the exported R6 classes for a programmatic workflow, ideal for advanced users and those integrating MRP into larger R projects.

Import shinymrp in scripts or R Markdown documents just like any other R package:

library(shinymrp)

Installation

To get started, install the latest development version of shinymrp from GitHub using remotes:

# If 'remotes' is not installed:
install.packages("remotes") 
remotes::install_github("mrp-interface/shinymrp")

The package installation does not automatically install all prerequisites. Specifically, shinymrp uses CmdStanR as the bridge to run Stan, a state-of-the-art platform for Bayesian modeling. Stan requires a modern C++ toolchain (compiler and GNU Make build utility).

Learn More

For detailed guidance, check our introductory vignette: Getting started with shinymrp.

This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.