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.

Provides different functionalities and calculations used in the world of basketball to analyze the statistics of the players, the statistics of the teams, the statistics of the quintets and the statistics of the plays. For more details of the calculations included in the package can be found in the book Basketball on Paper written by Dean Oliver.

Functions to compute upper Clopper-Pearson confidence limits of early life failure probabilities and required sample sizes of burn-in studies under further available information, e.g. from other products or technologies.

This software solves an Advection Bi-Flux Diffusive Problem using the Finite Difference Method FDM. Vasconcellos, J.F.V., Marinho, G.M., Zanni, J.H., 2016, Numerical analysis of an anomalous diffusion with a bimodal flux distribution. <doi:10.1016/j.rimni.2016.05.001>. Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. <doi:10.5902/2179460X13184>. In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t).