Fast and efficient computation of rolling statistics for time-series data.
roll is a package for R that provides fast and efficient computation of rolling statistics for time-series data.
Get the released version from CRAN:
install.packages("roll")Or the development version from GitHub:
# install.packages("devtools")devtools::install_github("jjf234/roll")roll_median, roll_min, roll_max, roll_any, and roll_all functions for computing rolling 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)
Updated src/Makevars and src/Makevars.win files to what the RcppArmadillo skeleton default now uses to more fully utilize OpenMP
RcppParallel package to one with the setThreadOptions functionDeprecated 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
New roll_sum and roll_prod functions for computing rolling sums and products, respectively, of time-series data
Added init.c file with calls to R_registerRoutines() and R_useDynamicSymbols(); also uses .registration = TRUE in useDynLib in NAMESPACE
Added intercept argument to roll_lm and roll_pcr functions
Turned on CXXSTD = CXX11 to enforce adherence to the C++11 standard
Added a section on examples to the README file
Fixed an issue in the src/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 scaling and centering of time-series data