Note: To report bugs with the package, please file an issue on the Github page. If you use this package, please cite the following: Kubinec, Robert. “Generalized Ideal Point Models for Time-Varying and Missing-Data Inference”. Working Paper. This package implements to kinds of time-varying ideal point models. Because these time-varying models are independent of the specific outcome used, time-varying ideal point models can be fit with any outcome/response supported by the package, including binary, ordinal, counts, continuous and positive-continuous data, in addition to the latent space model for binary data.
Background Conjoint survey experiments have become more popular in political science since the publication of Hainmueller, Hopkins and Yamamoto (2014). However, analysis of the statistical of power of conjoint experiments is difficult using standard parametric techniques because of the use of multiple treatments, interaction effects and paired vignettes. To that end, I have conducted the following simulation experiment to demonstrate the statistical properties of the conjoint experiment for my online survey experiment “Politically-Connected Firms and the Military-Clientelist Complex in North Africa” (see SocArchiv Draft).
This is a paper that was presented at the StanCon2018 conference on Bayesian inference with the Stan Hamiltonian Markov Chain Monte Carlo (MCMC) method. Video of my talk is available here. Introduction This notebook introduces idealstan, a new R package front-end to Stan that allows for flexible modeling of a class of latent variable models known as ideal point models. Ideal point modeling is a form of dimension reduction, and shares similarities with multi-dimensional scaling, factor analysis and item-response theory.