r-analyst

Run an end-to-end sociology analytics workflow in R with DiD, IV, RD, matching, and synthetic-control methods.

Updated Aug 23, 2026

Search Tags:#r #causal-inference #scm #sociology #panel-data #did #iv

Ready to run

npx skills add https://github.com/nealcaren/sociology-skillset --skill r-analyst

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: r-analyst
Source: https://github.com/nealcaren/sociology-skillset/tree/main/plugins/sociology-skillset/skills/r-analyst
Command: npx skills add https://github.com/nealcaren/sociology-skillset --skill r-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

End-to-end sociology analytics workflow in R enabling rigorous research design, data preparation, specification, estimation, and reporting.

Core Features & Use Cases

Phased design, data, specification, analysis, robustness, and reporting steps that align with best practices for social science research.
Supports DiD/TWFE, IV, RD, matching, synthetic control, event studies, and nonlinear models, with guidance through Phase 0 to Phase 5.
Use case: a panel dataset of local jurisdictions to estimate a policy's causal impact with robust robustness checks and publication-ready outputs.

Quick Start

Load your dataset and follow Phase 0–5 to reproduce a publishable sociology analysis in R.

Frequently Asked Questions about r-analyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run a difference-in-differences analysis in R for panel data?▼

To run a difference-in-differences analysis in R for panel data, this workflow guides you through phased steps from data preparation to TWFE specification, robustness checks, and publication-ready reporting.

What is the best way to design a sociology causal inference workflow in R?▼

The best way to design a sociology causal inference workflow in R is using a phased approach covering research design, data preparation, specification, estimation, and reporting to ensure reproducible sociological analysis.

Can I use synthetic control and instrumental variable methods together in R?▼

Yes, you can use synthetic control and instrumental variable methods in R. This workflow supports both techniques alongside matching and regression discontinuity within a unified phase-based sociological research framework.

Does this R workflow support event studies and nonlinear models?▼

This R workflow supports event studies and nonlinear models, providing estimation guidance and robustness checks to validate causal impacts across cross-sectional and panel datasets.

How do I create publication-ready outputs for sociological research in R?▼

To create publication-ready outputs for sociological research in R, follow the structured reporting phase which synthesizes estimation results, robustness checks, and specifications into reproducible deliverables.

When should I use matching versus regression discontinuity for causal impact estimation?▼

Use matching versus regression discontinuity for causal impact estimation based on your data structure; this workflow provides specification guidance for both methods to ensure rigorous sociological research design.