Package: adsoRptionMCMC 0.1.0

adsoRptionMCMC: Bayesian Estimation of Adsorption Isotherms via MCMC

Provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. This package enables users to fit non-linear and linear adsorption isotherm models—Freundlich, Langmuir, and Temkin—within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) <doi:10.1201/b14835>, and Gamerman & Lopes (2006) <doi:10.1201/9781482296426>.

Authors:Paul Angelo C. Manlapaz [aut, cre]

adsoRptionMCMC_0.1.0.tar.gz
adsoRptionMCMC_0.1.0.zip(r-4.7)adsoRptionMCMC_0.1.0.zip(r-4.6)adsoRptionMCMC_0.1.0.zip(r-4.5)
adsoRptionMCMC_0.1.0.tgz(r-4.6-any)adsoRptionMCMC_0.1.0.tgz(r-4.5-any)
adsoRptionMCMC_0.1.0.tar.gz(r-4.7-any)adsoRptionMCMC_0.1.0.tar.gz(r-4.6-any)
adsoRptionMCMC_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
adsoRptionMCMC/json (API)

# Install 'adsoRptionMCMC' in R:
install.packages('adsoRptionMCMC', repos = c('https://piey27.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/piey27/adsorptionmcmc/issues

On CRAN:

Conda:

2.00 score 1 stars 581 downloads 9 exports 10 dependencies

Last updated from:8f7fe2c788. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK126
source / vignettesOK161
linux-release-x86_64OK110
macos-release-arm64OK136
macos-oldrel-arm64OK129
windows-develOK98
windows-releaseOK87
windows-oldrelOK84
wasm-releaseOK84

Exports:mcmc_freundlichLMmcmc_freundlichNLMmcmc_langmuirLM1mcmc_langmuirLM2mcmc_langmuirLM3mcmc_langmuirLM4mcmc_langmuirNLMmcmc_temkinLMmcmc_temkinNLM

Dependencies:codalatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregSparseMsurvival