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-any)adsoRptionMCMC_0.1.0.zip(r-4.6-any)adsoRptionMCMC_0.1.0.zip(r-4.5-any)
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✨
DESCRIPTION
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 384 downloads 9 exports 10 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK115
source / vignettesOK148
linux-releaseOK125
macos-releaseOK180
macos-oldrelOK181
windows-develOK75
windows-releaseOK70
windows-oldrelOK75
wasm-releaseOK78

Exports:mcmc_freundlichLMmcmc_freundlichNLMmcmc_langmuirLM1mcmc_langmuirLM2mcmc_langmuirLM3mcmc_langmuirLM4mcmc_langmuirNLMmcmc_temkinLMmcmc_temkinNLM

Dependencies:codalatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregSparseMsurvival