Univariate and multivariate versions of risk-based control charts. Univariate versions of control charts, such as the risk-based version of X-bar, Moving Average (MA), Exponentially Weighted Moving Average Control Charts (EWMA), and Cumulative Sum Control Charts (CUSUM) charts. The risk-based version of the multivariate T2 control chart. Plot and summary functions. Kosztyan et. al. (2016) <doi:10.1016/j.eswa.2016.06.019>.

Issues RPC-JSON calls to 'bitcoind', the daemon of Bitcoin Cash (BCH), to
    extract transaction data from the blockchain. BCH is a fork of Bitcoin that permits
    a greater number of transactions per second. A BCH daemon is available under an MIT
    license from the Bitcoin Unlimited website <https://www.bitcoinunlimited.info>.

Implementing the BDAT tree taper Fortran routines, which were
  developed for the German National Forest Inventory (NFI), to calculate 
  diameters, volume, assortments, double bark thickness and biomass for
  different tree species based on tree characteristics and sorting information.
  See Kublin (2003) <doi:10.1046/j.1439-0337.2003.00183.x> for details.

Provide estimation and data generation tools for the quantile generalized beta 
        regression model. For details, see Bourguignon, Gallardo and Saulo <arXiv:2110.04428>
        The package also provides tools to perform covariates selection.

Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points, breakpoints, structural breaks, or join-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.