r - Generating random numbers from various distributions in CUDA -

i playing around doing mcmc on gpu, , need implementations various samplers, written cuda.

most of posts i've seen on stackoverflow relate uniform, binomial, , normal sampling. there libraries allow me simplicity , variety of d-p-q-r functions in r (see this page)?

i able sample gamma, normal, binomial, , inverse distributions used in bayesian problems (inverse chi square, inverse gamma), , prefer not have write own using inverse probability transforms , acceptance-rejection sampling.

for gamma distribution, use @ moment. gsl function modified work curand.

__device__ double ran_gamma (curandstate localstate, const double a, const double b){ /* assume > 0 */  if (a < 1){     double u = curand_uniform_double(&localstate);     return ran_gamma (localstate, 1.0 + a, b) * pow (u, 1.0 / a); }  {     double x, v, u;     double d = - 1.0 / 3.0;     double c = (1.0 / 3.0) / sqrt (d);      while (1){         do{             x = curand_normal_double(&localstate);             v = 1.0 + c * x;         } while (v <= 0);          v = v * v * v;         u = curand_uniform_double(&localstate);          if (u < 1 - 0.0331 * x * x * x * x)              break;          if (log (u) < 0.5 * x * x + d * (1 - v + log (v)))             break;     }     return b * d * v; } }