Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>).

Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.

Enable users to evaluate long-term trends using a Generalized 
    Additive Modeling (GAM) approach. The model development includes selecting a 
    GAM structure to describe nonlinear seasonally-varying changes over time, 
    incorporation of hydrologic variability via either a river flow or salinity, 
    the use of an intervention to deal with method or laboratory changes 
    suspected to impact data values, and representation of left- and 
    interval-censored data. The approach has been applied to water quality data 
    in the Chesapeake Bay, a major estuary on the east coast of the United 
    States to provide insights to a range of management- and research-focused 
    questions.  Methodology described in Murphy (2019) 
    <doi:10.1016/j.envsoft.2019.03.027>.

Barzilai-Borwein spectral methods for solving nonlinear
        system of equations, and for optimizing nonlinear objective
        functions subject to simple constraints. A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("BB").

Implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>).  
       The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. 
       For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.