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>.

Analyzes autocorrelation and partial autocorrelation using surrogate methods and 
  bootstrapping, and computes the acceleration constants for the vectorized moving block 
  bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated 
  intervals and estimates partial autocorrelations using Durbin-Levinson. This package 
  calculates the autocorrelation power spectrum, computes cross-correlations between two 
  time series, computes bandwidth for any time series, and performs autocorrelation frequency 
  analysis. It also calculates the periodicity of a time series.