Title: Efficient Rolling / Windowed Operations
Description: Provides fast and efficient routines for
common rolling / windowed operations. Routines for the
efficient computation of windowed mean, median,
sum, product, minimum, maximum, standard deviation
Suggests: zoo, microbenchmark, testthat, RcppArmadillo
Packaged: 2015-04-04 23:07:51 UTC; kevinushey

Title: Rcpp bindings for Sequential Monte Carlo
Author: Dirk Eddelbuettel and Adam M. Johansen
Description: This package provides R with access to the Sequential
Monte Carlo Template Classes by Johansen (Journal of
At present, two additional examples have been added, and the first
example from the JSS paper has been extended. Further

Monte Carlo Template Classes by Johansen (Journal of Statistical
example from the JSS paper has been extended. Further integration

Title: Rcpp Bindings for Sequential Monte Carlo
Description: R access to the Sequential Monte Carlo Template Classes
by Johansen (Journal of Statistical Software, 2009, v30, i6) is provided.

Author: Dirk Eddelbuettel, Adam M. Johansen and Leah F. South

by Johansen <doi:10.18637/jss.v030.i06> is provided. At present, four
additional examples have been added, and the first example from the JSS
paper has been extended. Further integration and extensions are planned.

Author: Dirk Eddelbuettel, Adam M. Johansen, Leah F. South and Ilya Zarubin

Title: R Interface to the Shark Machine Learning Library
Description: An R interface to the C++/Boost Shark machine learning library.
SystemRequirements: Boost 1.49 or later (via BH package)
Packaged: 2015-08-17 20:05:31 UTC; drunkeneye

SystemRequirements: Boost 1.52 or later (via BH package)
Packaged: 2016-06-29 10:09:38 UTC; drunkeneye

SystemRequirements: C++11, Boost 1.52 or later (via BH package)

Title: 'Rcpp' Bindings for the 'simdjson' Header-Only Library for
Description: The 'JSON' format is ubiquitous for data interchange, and the
'simdjson' library written by Daniel Lemire (and many contributors) provides
a high-performance parser for these files which by relying on parallel 'SIMD'
instruction manages to parse these files as faster than disk speed. See the
<arXiv:1902.08318> paper for more details about 'simdjson'.  This package is
at present only a very thin and incomplete wrapper and does not aim to replace
the existing and excellent 'JSON' packages for R. But it does already validate