Package: sobol 1.0.1

sobol: Quasi-Monte Carlo Sobol Sequence Generator

Provides a fast and efficient implementation of Sobol sequences for quasi-Monte Carlo methods. The Sobol sequence is a low-discrepancy sequence with the property that for all values of N, its subsequence x1, ..., xN has a low discrepancy. It can be used to generate quasi-random numbers for use in Monte Carlo integration and other simulation methods. This implementation is based on the algorithms described by Bratley and Fox (1988) <doi:10.1145/42288.214372> and uses direction numbers from Joe and Kuo (2008) <doi:10.1145/1358628.1358630>. The package includes both batch and incremental interfaces with support for arbitrary starting indices and reproducible sequences. It uses 'Rcpp' for efficient 'C++' integration.

Authors:Angel Robles [aut, cre]

sobol_1.0.1.tar.gz
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sobol_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
sobol/json (API)

# Install 'sobol' in R:
install.packages('sobol', repos = c('https://alrobles.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alrobles/sobol/issues

Pkgdown/docs site:https://alrobles.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

5.02 score 13 scripts 312 downloads 8 exports 3 dependencies

Last updated from:8d2b2c306d. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK164
linux-devel-x86_64OK137
source / vignettesOK190
linux-release-arm64OK145
linux-release-x86_64OK129
macos-release-arm64OK130
macos-release-x86_64OK186
macos-oldrel-arm64OK136
macos-oldrel-x86_64OK174
windows-develOK124
windows-releaseOK140
windows-oldrelOK121
wasm-releaseOK106

Exports:sobol_designsobol_dimensionssobol_generatorsobol_indexsobol_nextsobol_next_nsobol_pointssobol_skip_to

Dependencies:backportscheckmateRcpp

Getting Started with sobol
Installation | 1. The quickest path: sobol_design() | 2. What makes sobol_design special? | 3. Going one level deeper: raw points | 4. Incremental generation with sobol_generator() | 5. Reproducibility and parallel optimisation | 6. Advanced: chaining generators for adaptive sampling | 7. Performance notes | Next steps | Acknowledgements | References | See Also

Last update: 2026-05-13
Started: 2026-04-23

Introduction to Sobol Sequences
Introduction | Historical Background | Basic Usage | Key Features | 1. Incremental Generation | 2. Skip-Ahead Capability | 3. Batch Generation | Comparison with Random Sampling | Monte Carlo Integration Example | High-Dimensional Applications | Performance Considerations | Advanced Usage: Parallel Processing | Best Practices | References | Conclusion

Last update: 2026-04-23
Started: 2026-04-23