xifa 0.1.3

Accelerated Item Factor Analysis

XIFA: Accelerated Item Factor Analysis

xifa is a python package for conducting item factor analysis (IFA), a representative multivariate technique in psychometrics.

xifa is build on jax, a package for Autograd and XLA (accelerated linear algebra). Hence, xifa can run IFA on GPUs and TPUs to hugely speed up the training process. That is why we call it Accelerated IFA.

xifa implements a vectorized Metropolis-Hastings Robbins-Monro (MH-RM) algorithm to calculate a marginal maximum likelihood (MML) estimate. MH-RM is one of the states-of-art algorithms for high dimensional IFA (Cai, 2010). The vectorized version is designed for parallel computing with GPUs of TPUs. The vectorized MH-RM includes two stages. The first stage updates the parameter estimate by a stochastic expectation-maximization (StEM) algorithm. The second stage conducts stochastic approximation (SA) to refine the estimate.

In the current version (0.1.3), xifa supports ordinal data IFA with graded response model (GRM; Semejima, 1969) and generalized partial credit model (GPCM; Muraki, 1992). The analysis can be either exploratory or confirmatory.

For a tutorial, please see the IPIP 50 Items Example.