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dqrrademacher
for drawing Rademacher weights (Kyle Butts in #50 fixing #49)dqrmvnorm
sampling from a multivariate normal distribution. This uses the methods implemented in the mvtnorm
package and uses dqrnorm
.dqrng
’s RNG is based on R’s RNG, which used to advance R’s RNG state. The implementation has been changed to preserve R’s RNG state, which is less surprising but can change the outcome of current scripts. (#44 fixing #43)inline
and include required standard headers (Aaron Lun in #29 fixing #28)long_jump()
for Xo(ro)shiro as alternative to jump()
providing fewer streams with longer period.dqsample
and dqsample.int
using an unbiased sampling algorithm.R_unif_index()
instead of unif_rand()
to retrieve random data from R’s RNG in generateSeedVectors()
.int
is used for seeding (Aaron Lun in #10)
dqrng::dqset_seed()
expects a Rcpp::IntegerVector
instead of an int
generateSeedVectors()
for generating a list of random int
vectors from R’s RNG. These vectors can be used as seed (Aaron Lun in #10).std::random_device
as source of the default seed, since std::random_device
is deterministic with MinGW (c.f. #2)dqrng_distribution.h
can now be used independently of Rcppxorshift.hpp
and xoroshiro.hpp
with xoshiro.h
. This implementation is directly derived from the original C implementations. It provides v1.0 of Xoroshiro128+ and Xoshiro256+.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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