Provides tools for classical parameter estimation of adsorption isotherm models, including both 
  linear and nonlinear forms of the Freundlich, Langmuir, and Temkin isotherms. This package allows 
  users to fit these models to experimental data, providing parameter estimates along with fit statistics 
  such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Error metrics are 
  computed to evaluate model performance, and the package produces model fit plots with bootstrapped 95% 
  confidence intervals. Additionally, it generates residual plots for diagnostic assessment of the models. 
  Researchers and engineers in material science, environmental engineering, and chemical engineering can 
  rigorously analyze adsorption behavior in their systems using this straightforward, non-Bayesian approach. 
  For more details, see Harding (1907) <doi:10.2307/2987516>.

Provides cross-validation tools for adsorption isotherm models, supporting both linear and non-linear forms. 
    Current methods cover commonly used isotherms including the Freundlich, Langmuir, and Temkin models. 
    This package implements K-fold and leave-one-out cross-validation (LOOCV) with optional clustering-based
    fold assignment to preserve underlying data structures during validation. Model predictive performance is assessed 
    using mean squared error (MSE), with optional graphical visualization of fold-wise MSEs to support intuitive evaluation of model accuracy. 
    This package is intended to facilitate rigorous model validation in adsorption studies and aid researchers in selecting robust isotherm models.
    For more details, see Montgomery et al. (2012) <isbn: 978-0-470-54281-1>, Lumumba et al. (2024) <doi:10.11648/j.ajtas.20241305.13>, and Yates et al. (2022) <doi:10.1002/ecm.1557>.

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>.