Package: plgp 1.1-12
plgp: Particle Learning of Gaussian Processes
Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) <arxiv:0909.5262>. The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos.
Authors:
plgp_1.1-12.tar.gz
plgp_1.1-12.zip(r-4.5)plgp_1.1-12.zip(r-4.4)plgp_1.1-12.zip(r-4.3)
plgp_1.1-12.tgz(r-4.4-x86_64)plgp_1.1-12.tgz(r-4.4-arm64)plgp_1.1-12.tgz(r-4.3-x86_64)plgp_1.1-12.tgz(r-4.3-arm64)
plgp_1.1-12.tar.gz(r-4.5-noble)plgp_1.1-12.tar.gz(r-4.4-noble)
plgp_1.1-12.tgz(r-4.4-emscripten)plgp_1.1-12.tgz(r-4.3-emscripten)
plgp.pdf |plgp.html✨
plgp/json (API)
| # Install 'plgp' in R: |
| install.packages('plgp', repos = c('https://rbgramacy.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 2 years agofrom:6c89abe8b7. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 08 2024 |
| R-4.5-win-x86_64 | OK | Sep 08 2024 |
| R-4.5-linux-x86_64 | OK | Sep 08 2024 |
| R-4.4-win-x86_64 | OK | Sep 08 2024 |
| R-4.4-mac-x86_64 | OK | Sep 08 2024 |
| R-4.4-mac-aarch64 | OK | Sep 08 2024 |
| R-4.3-win-x86_64 | OK | Sep 08 2024 |
| R-4.3-mac-x86_64 | OK | Sep 08 2024 |
| R-4.3-mac-aarch64 | OK | Sep 08 2024 |
Exports:addpall.CGPaddpall.ConstGPaddpall.GPalc.adaptalc.const.adaptalc.ConstGPalc.GPcalc.alcscalc.eciscalc.eiscalc.entscalc.iecicalc.ieciscalc.ktKik.xcalc.varscalc2.ktKik.xcovarcovar.sepcovar.simcv.foldsdata.CGPdata.CGP.adaptdata.ConstGP.improvdata.GPdata.GP.improvdist2covar.symmdistancedraw.CGPdraw.ConstGPdraw.GPEIei.adaptentropyentropy.adaptentropy.bvsbexp2d.Cfindmin.ConstGPfindmin.GPgetmap.CGPgetmap.GPieci.adaptieci.const.adaptieci.ConstGPieci.GPinit.CGPinit.ConstGPinit.GPlatents.CGPlpost.GPlpredprob.CGPlpredprob.ConstGPlpredprob.GPmindist.adaptpapplyparams.CGPparams.ConstGPparams.GPphistPLPL.clearPL.envpred.CGPpred.ConstGPpred.GPpred.mean.GPprior.CGPprior.ConstGPprior.GPpropagate.CGPpropagate.ConstGPpropagate.GPrectscalerectunscalerenorm.lweightsrenorm.weightsresampletquants
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Particle Learning of Gaussian Processes | plgp-package |
| Add data to pall | addpall.CGP addpall.ConstGP addpall.GP |
| Supply GP data to PL | data.CGP data.CGP.adapt data.ConstGP data.ConstGP.improv data.GP data.GP.improv |
| Metropolis-Hastings draw for GP parameters | draw.CGP draw.ConstGP draw.GP |
| 2-d Exponential Hessian Data | exp2d.C |
| Initialize particles for GPs | init.CGP init.ConstGP init.GP |
| Log-Predictive Probability Calculation for GPs | lpredprob.CGP lpredprob.ConstGP lpredprob.GP |
| Extending apply to particles | papply |
| Extract parameters from GP particles | params.CGP params.ConstGP params.GP |
| Particle Learning Skeleton Method | PL PL.env plgp |
| Prediction for GPs | pred.CGP pred.ConstGP pred.GP |
| Generate priors for GP models | prior.CGP prior.ConstGP prior.GP |
| PL propagate rule for GPs | propagate.CGP propagate.ConstGP propagate.GP |
| Un/Scale data in a bounding rectangle | rectscale rectunscale |
