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  "Title": "Bayesian Estimation of Adsorption Isotherms via MCMC",
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  "Author": "Paul Angelo C. Manlapaz [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-1203-2064>)",
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  "Description": "Provides tools for Bayesian parameter estimation of\nadsorption isotherm models using Markov Chain Monte Carlo\n(MCMC) methods. This package enables users to fit non-linear\nand linear adsorption isotherm models—Freundlich, Langmuir, and\nTemkin—within a probabilistic framework, capturing uncertainty\nand parameter correlations. It provides posterior summaries,\n95% credible intervals, convergence diagnostics (Gelman-Rubin),\nand visualizations through trace and density plots. With this R\npackage, researchers can rigorously analyze adsorption behavior\nin environmental and chemical systems using robust Bayesian\ninference. For more details, see Gilks et al. (1995)\n<doi:10.1201/b14835>, and Gamerman & Lopes (2006)\n<doi:10.1201/9781482296426>.",
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