The FLEX method, developed by Yoon and Choi (2013) <doi:10.1007/978-3-642-33042-1_21>, performs least squares estimation for fuzzy predictors and outcomes, generating crisp regression coefficients by minimizing the distance between observed and predicted outcomes. It also provides functions for fuzzifying data and inference tasks, including significance testing, fit indices, and confidence interval estimation.

A faster implementation of Bayesian Causal Forests (BCF; Hahn et al. (2020) <doi:10.1214/19-BA1195>), which uses regression tree ensembles to estimate the conditional average treatment effect of a binary treatment on a scalar output as a function of many covariates. This implementation avoids many redundant computations and memory allocations present in the original BCF implementation, allowing the model to be fit to larger datasets. The implementation was originally developed for the 2022 American Causal Inference Conference's Data Challenge. See Kokandakar et al. (2023) <doi:10.1353/obs.2023.0024> for more details.

The main function kcca implements a general framework for
  k-centroids cluster analysis supporting arbitrary distance measures
  and centroid computation. Further cluster methods include hard
  competitive learning, neural gas, and QT clustering. There are
  numerous visualization methods for cluster results (neighborhood
  graphs, convex cluster hulls, barcharts of centroids, ...), and
  bootstrap methods for the analysis of cluster stability.

An implementation of multiple regression models for 
    count data. These include various forms of the negative binomial (NB-1, 
    NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other
    compound Poisson distributions, the Generalized Waring model, etc.
    Information on the different forms of the negative binomial are
    described by Greene (2008) <doi:10.1016/j.econlet.2007.10.015>. For
    treatises on count models, see Cameron and Trivedi (2013) 
    <doi:10.1017/CBO9781139013567> and Hilbe (2012)
    <doi:10.1017/CBO9780511973420>. For the implementation of
    under-reporting in count models, see Wood et al. (2016)
    <doi:10.1016/j.aap.2016.06.013>. For prediction methods in random
    parameter models, see Wood and Gayah (2025)
    <doi:10.1016/j.aap.2025.108147>. For estimating random parameters using
    maximum simulated likelihood, see Greene and Hill (2010)
    <doi:10.1108/S0731-9053(2010)26>; Gourieroux and Monfort (1996)
    <doi:10.1093/0198774753.001.0001>; or Hensher et al. (2015)
    <doi:10.1017/CBO9781316136232>.