Package: cPseudoMaRg 1.0.1
cPseudoMaRg: Constructs a Correlated Pseudo-Marginal Sampler
The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) <arxiv:1511.04992>. If the 'rho=' argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) <doi:10.1214/07-AOS574>. The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen.
Authors:
cPseudoMaRg_1.0.1.tar.gz
cPseudoMaRg_1.0.1.zip(r-4.7-any)cPseudoMaRg_1.0.1.zip(r-4.6-any)cPseudoMaRg_1.0.1.zip(r-4.5-any)
cPseudoMaRg_1.0.1.tgz(r-4.6-any)cPseudoMaRg_1.0.1.tgz(r-4.5-any)
cPseudoMaRg_1.0.1.tar.gz(r-4.7-any)cPseudoMaRg_1.0.1.tar.gz(r-4.6-any)
cPseudoMaRg_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cPseudoMaRg/json (API)
| # Install 'cPseudoMaRg' in R: |
| install.packages('cPseudoMaRg', repos = c('https://tbrown122387.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:20c9c34f29. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 102 | ||
| source / vignettes | OK | 144 | ||
| linux-release | OK | 91 | ||
| macos-release | OK | 75 | ||
| macos-oldrel | OK | 79 | ||
| windows-devel | OK | 65 | ||
| windows-release | OK | 77 | ||
| windows-oldrel | OK | 74 | ||
| wasm-release | OK | 81 |
Exports:isBadNummakeCPMSampler
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| checks if a log-density evaluation is not a valid number | isBadNum |
| correlated pseudo-marginal: generates functions that output a big vector | makeCPMSampler |
| calculates the posterior mean point estimate | mean.cpmResults |
| plots a cpmResults object | plot.cpmResults |
| prints a cpmResults object | print.cpmResults |
