copre (version 0.2.1)

copre: Copula Resampling

Description

A function that samples predictive distributions for univariate continuous data using the bivariate Gaussian copula.

Usage

copre(
  data,
  N,
  k,
  rho = 0.91,
  grd_res = 1000,
  nthreads = parallel::detectCores(),
  gpu = FALSE,
  gpu_path = NULL,
  gpu_odir = NULL,
  gpu_seed = 1234
)

Value

A copre_result object, whose underlying structure is a list which contains the following components:

Arguments

data

The data from which to sample predictive distributions.

N

The number of unobserved data points to resample for each chain.

k

The number of predictive distributions to sample.

rho

A scalar concentration parameter.

grd_res

The number of points on which to evaluate the predictive distribution.

nthreads

The number of threads to call for parallel execution.

gpu

A logical value indicating whether or not to use the CUDA implementation of the algorithm.

gpu_path

The path to the CUDA implementation source code.

gpu_odir

A directory to output the compiled CUDA code.

gpu_seed

A seed for the CUDA random variates.

References

Fong, E., Holmes, C., Walker, S. G. (2021). Martingale Posterior Distributions. arXiv. DOI: tools:::Rd_expr_doi("10.48550/arxiv.2103.15671")

Examples

res_cop <- copre(rnorm(50), 10, 10, nthreads = 1)