Research Methods Library

Recommend statistical, econometric, causal inference, or machine learning methods for a research question or data analysis task: which statistical test or model fits the data, how to estimate a causal or treatment effect, how to analyze an A/B test or experiment, how many observations are needed (power and sample size), how to handle panel, time-series, survey, text, or survival data, and how to build predictive or credit-scoring models. Describe the question, the data (cross-section, panel, time series, experiment), how the treatment or exposure was assigned, and the outcome. Returns candidate methods with when each applies, key assumptions, R and Python packages, and the library sections that teach them.

Format references for works in the library in APA, Chicago, MLA, Harvard, BibTeX, or RIS. Pass the ids of the sections you used to get a deduplicated reference list plus the section links, or pass source ids to cite whole works. With no ids, returns a reference for every work in the library.

Read one section of the library by its section id. Returns the section in Markdown with its code, the section URL, and the citation of the work it comes from. Long sections come in parts; the text names the id of the next part.

Get runnable code from the library for a specific statistical, econometric, causal inference, or machine learning task, in R, Python, or Julia. Suited to requests for code that carries out a named estimator, test, or modeling step; it returns code with brief context, not an explanation of the method. Results can be restricted to one language or one work. Returns code blocks with the section they come from, the packages they use, a link, and a citation.

List the works in the library (textbooks, an R package, and research articles) with their topics, code languages, section counts, licenses, links, and how to cite each one. Suited to questions about what the library covers or which work to consult. Takes no parameters and returns one entry per work.

Search sections of textbooks and an R package on research methods and data analysis: statistics and hypothesis testing, regression and generalized linear models, causal inference (difference-in-differences, synthetic control, regression discontinuity, instrumental variables, matching, double machine learning), econometrics, experimental design, survey methods, marketing research, credit scoring, machine learning, and AI. Returns matching sections with their ids, a link, a snippet, the code languages they contain, and a citation.