Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2025; <doi:10.48550/arXiv.2405.17290>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).

Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen, 2009) <DOI:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <DOI:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <DOI:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <DOI:10.1027/1614-2241/a000131>.

Allows users to seamlessly query several 'CDC PLACES' APIs (<https://data.cdc.gov/browse?q=PLACES%20&sortBy=relevance>)
    by geography, state, measure, and release year. This package also contains a
    function to explore the available measures for each release year.

Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <https://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.