Implements an interface to Minecraft (Bedrock Edition) worlds. Supports the analysis and management of these worlds and game saves.

Benchmarking of arbitrary R code, inspired by similar functionality
 in a Perl module, is implemented via a simple wrapper around system.time().
 Given a specification of the benchmarking process (counts of replications,
 evaluation environment) and an arbitrary number of expressions, the function
 evaluates each of the expressions in the specified environment, replicating the
 evaluation as many times as specified, and returning the results conveniently
 wrapped into a data frame.

An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted."

Tool-set to support Bayesian evidence synthesis.  This
    includes meta-analysis, (robust) prior derivation from historical
    data, operating characteristics and analysis (1 and 2 sample
    cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19>
    for details on applying this package while Neuenschwander et al. (2010)
    <doi:10.1177/1740774509356002> and Schmidli et al. (2014)
    <doi:10.1111/biom.12242> explain details on the methodology.

Contains functions to generate random numbers
        from the beta distribution and random vectors from the
        Dirichlet distribution.

A robust backfitting algorithm for additive models based on (robust) local polynomial 
             kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and
             it computes predictions for points outside the training set if desired. See
             Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and 
             Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.

Implements the Residual-Based Fully Modified Vector Autoregression
    (RBFM-VAR) estimator of Chang (2000) <doi:10.1017/S0266466600166071>.
    The RBFM-VAR procedure extends Phillips (1995) FM-VAR to handle any unknown
    mixture of I(0), I(1), and I(2) components without prior knowledge of the
    number or location of unit roots. Provides automatic lag selection via
    information criteria (AIC, BIC, HQ), long-run variance estimation using
    Bartlett, Parzen, or Quadratic Spectral kernels with Andrews (1991)
    <doi:10.2307/2938229> automatic bandwidth selection, Granger non-causality
    testing with asymptotically chi-squared Wald statistics, impulse response
    functions (IRF) with bootstrap confidence intervals, forecast error variance
    decomposition (FEVD), and out-of-sample forecasting.