A collection of miscellaneous functions for 
    copying objects to the clipboard ('Copy');
    manipulating strings ('concat', 'mgsub', 'trim', 'verlan'); 
    loading or showing packages ('library_with_dep', 'require_with_dep', 
    'sessionPackages'); 
    creating or testing for named lists ('nlist', 'as.nlist', 'is.nlist'), 
    formulas ('is.formula'), empty objects ('as.empty', 'is.empty'), 
    whole numbers ('as.wholenumber', 'is.wholenumber'); 
    testing for equality ('almost.equal', 'almost.zero') and computing 
    uniqueness ('almost.unique'); 
    getting modified versions of usual functions ('rle2', 'sumNA'); 
    making a pause or a stop ('pause', 'stopif'); 
    converting into a function ('as.fun'); 
    providing a C like ternary operator ('condition %?% true %:% false'); 
    finding packages and functions ('get_all_pkgs', 'get_all_funs');
    and others ('erase', '%nin%', 'unwhich', 'top', 'bot', 'normalize').

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.

We implemented a Bayesian-statistics approach for 
        subtraction of incoherent scattering from neutron total-scattering data. 
        In this approach, the estimated background signal associated with 
        incoherent scattering maximizes the posterior probability, which combines 
        the likelihood of this signal in reciprocal and real spaces with the prior 
        that favors smooth lines. The description of the corresponding approach 
        could be found at Gagin and Levin (2014) <DOI:10.1107/S1600576714023796>.