Multiple Hypothesis Testing on an Aggregation Tree Method (TEAM)
An implementation of the TEAM algorithm to identify local differences between two (e.g. case and control) independent, univariate distributions, as described in J Pura, C Chan, and J Xie (2019) <arXiv:1906.07757>. The algorithm is based on embedding a multiple-testing procedure on a hierarchical structure to identify high-resolution differences between two distributions. The hierarchical structure is designed to identify strong, short-range differences at lower layers and weaker, but long-range differences at increasing layers. TEAM yields consistent layer-specific and overall false discovery rate control.

Logistic Regression (BayesLogit)
Perform posterior simulation for binomial and multinomial logistic regression using the Polya-Gamma latent variable technique. This method is fully automatic, exact, and fast. A routine to efficiently sample from the Polya-Gamma class of distributions is included based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>.

Intraclass Correlations for Quantifying Inter-Rater Reliability (irrICC)
Calculates various intraclass correlation coefficients used to quantify inter-rater and intra-rater reliability. The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): ‘Handbook of Inter-Rater Reliability,’ 4th edition, Advanced Analytics, LLC.

Estimate Bunching (bunching)
Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <DOI:10.1093/qje/qjr013>, with lots of functionality to set plot options.

Hierarchical Shrinkage Stan Models for Biomarker Selection (hsstan)
Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2018) <arXiv:1810.02406>).