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', 'normlize').

Identification of Bull and Bear States of the Market
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.

Bayesian bandwidth estimation for functional kernel regression
with unknown error density
Estimate optimal bandwidth for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals

Bayesian bandwidth estimation for the functional kernel
regression with unknown error density
Estimate optimal bandwidths for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals simultaneously in a scalar-on-function regression