Development, simulation testing, and implementation of management procedures for fisheries 
    (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).

A companion to the Chinese book ``Modern Statistical Graphics''.

Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2019) <doi:10.18637/jss.v091.i04>.

Implements the three parallel forecast combinations of Markov Switching GARCH and extreme learning machine model along with the selection of appropriate model for volatility forecasting. For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>.

This is the library that can serialize or unserialize MessagePack format data.

Computes the degrees of freedom of the lasso,
        elastic net, generalized elastic net and adaptive lasso based
        on the generalized path seeking algorithm.  The optimal model
        can be selected by model selection criteria including Mallows'
        Cp, bias-corrected AIC (AICc), generalized cross validation
        (GCV) and BIC.

Provides new functions info(), warn() and error(), similar to message(),
    warning() and stop() respectively. However, the new functions can have a 'level'
    associated with them, so that when executed the global level option determines whether
    they are shown or not. This allows debug modes, outputting more information. The can also
    output all messages to a log file.

Allows for the computation of mSHAP values on
    two-part models as proposed  by Matthews, S. and Hartman, B. 
    (2021) <arXiv:2106.08990>.
    Also contains functions for simple plotting of
    the results (or any SHAP values). For information about the
    TreeSHAP algorithm that mSHAP builds on, see Lundberg, S.M.,
    Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz,
    R., Himmelfarb, J., Bansal, N., Lee, S.I. (2020)
    <doi:10.1038/s42256-019-0138-9>.