The set of teacher/class lessons is completed with a column that
    allocates a day to each lesson, so that the distribution of lessons by
    day, by class, and by teacher is as uniform as possible.
    <https://vlad.bazon.net/>.

Allows clinicians and researchers to compute daily dose (and subsequently days' supply) for prescription refills using the following methods: Fixed window, fixed tablet, defined daily dose (DDD), and Random Effects Warfarin Days' Supply (REWarDS). Daily dose is the computed dose that the patient takes every day. For medications with fixed dosing (e.g. direct oral anticoagulants) this is known and does not need to be estimated. For medications with varying dose such as warfarin, however, the daily dose should be assumed or estimated to allow measurement of drug exposure. Days’ supply is the number of days that patients’ supply of medication will last after each prescription fill. Estimating days’ supply is necessary to calculate drug exposure. The package computes days’ supply and daily dose at both the prescription and patient levels. Results at the prescription level are denoted with “-Rx-” and those at patient level are denoted with “-Pt-”.

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'.