Tools for exporting 'PostgreSQL' tables to 'Parquet' files, with
    support for chunked writes, column type overrides, and timezone-aware
    timestamp handling. Includes functions for maintaining a local 'Parquet'
    data library sourced from 'WRDS' (Wharton Research Data Services), with
    update-checking based on table metadata, and archive management utilities
    for versioning local data files. See Gow and Ding (2024)
    "Empirical Research in Accounting: Tools and Methods"
    <doi:10.1201/9781003456230>.

Provides methods for (auto)covariance/correlation function estimation 
    in change point regression with stationary errors circumventing the pre-estimation
    of the underlying signal of the observations. Generic, first-order, (m+1)-gapped,
    difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) <doi:10.48550/arXiv.1905.04578>. Bias-reducing, second-order, (m+1)-gapped, 
    difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) 
    <doi:10.1111/sjos.12256>. Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) <doi:10.3150/15-BEJ782>. 
    It also includes a general projection-based method for covariance matrix estimation.

Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package 'BayesTree'.

A metric called 'Density-Based Clustering Validation index' (DBCV) index to evaluate clustering results, following the <https://github.com/pajaskowiak/clusterConfusion/blob/main/R/dbcv.R> 'R' implementation by Pablo Andretta Jaskowiak. Original 'DBCV' index article: Moulavi, D., Jaskowiak, P. A., Campello, R. J., Zimek, A., and Sander, J. (April 2014), "Density-based clustering validation", Proceedings of SDM 2014 -- the 2014 SIAM International Conference on Data Mining (pp. 839-847), <doi:10.1137/1.9781611973440.96>. A more recent article on the 'DBCV' index: Chicco, D., Sabino, G.; Oneto, L.; Jurman, G. (August 2025), "The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters", PeerJ Computer Science 11:e3095 (pp. 1-), <doi:10.7717/peerj-cs.3095>.