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Implemented efficient algorithms for roll_median and
roll_quantile functions
Fixed an issue for weights argument if single value
(#45)
New roll_crossprod function for computing rolling
and expanding crossproducts of time-series data
Restructured the C++ header files to be used by other packages
Added hex sticker and improved documentation (#27, #28, #30)
New roll_quantile function for computing rolling and
expanding quantiles of time-series data
roll_quantile function is not calculated using an
online algorithmFixed an issue in the roll_min and
roll_max functions (#32)
Added vector support to each function (#20)
Implemented efficient algorithms for roll_min and
roll_max functions
New roll_idxmin and roll_idxmax
functions for computing rolling and expanding indices of minimums and
maximums, respectively, of time-series data (#22)
New roll_median, roll_min,
roll_max, roll_any, and roll_all
functions for computing rolling and expanding medians, minimums,
maximums, any, and all, respectively, of time-series data (#4, #13, #14)
roll_median, roll_min, and
roll_max functions are not calculated using online
algorithmsAdded online argument to process observations using
online algorithms by default
roll_lm function now returns standard errors (#7)
Simplified checks for width and min_obs
arguments (#3)
Added y argument to roll_cov and
roll_cor functions (#2)
Deprecated less common functions (roll_eigen,
roll_vif, and roll_pcr) and arguments
(scale and center in the roll_lm
function); also removed the parallel_for argument in favor
of a new approach used internally
roll_sum and roll_prod functions for
computing rolling and expanding sums and products, respectively, of
time-series dataintercept argument to roll_lm and
roll_pcr functionssrc/Makevars and
src/Makevars.win files (#1)roll_lm and roll_pcr functions have
been enhanced:
y can now be a matrix or xts object with multiple
dependent variables
Added shorthand arguments for center and
scale
New roll_scale function for computing rolling and
expanding scaling and centering of time-series data
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They may not be fully stable and should be used with caution. We make no claims about them.