A simulations-first sample size determination package that aims at making sample size formulae obsolete for most easily computable statistical experiments ; the main envisioned use case is clinical trials. The proposed clinical trial must be written by the user in the form of a function that takes as argument a sample size and returns a boolean (for whether or not the trial is a success). The 'adsasi' functions will then use it to find the correct sample size empirically. The unavoidable mis-specification is obviated by trying sample size values close to the right value, the latter being understood as the value that gives the probability of success the user wants (usually 80 or 90% in biostatistics, corresponding to 20 or 10% type II error).

Interact with Google Ads Data Hub API <https://developers.google.com/ads-data-hub/reference/rest>. The functionality allows to fetch customer details, submit queries to ADH.

Solving high-dimensional double sparse linear regression via an iterative hard thresholding algorithm. Furthermore, the method is extended to jointly estimate multiple graphical models.  For more details, please see <https://www.jmlr.org/papers/v25/23-0653.html> and <doi:10.48550/arXiv.2503.18722>.

Model adsorption behavior using classical isotherms, including Langmuir,
    Freundlich, Brunauer–Emmett–Teller (BET), and Temkin models. The package supports
    parameter estimation through both linearized and non-linear fitting techniques and
    generates high-quality plots for model diagnostics. It is intended for environmental
    scientists, chemists, and researchers working on adsorption phenomena in soils,
    water treatment, and material sciences. Functions are compatible with base 'R' and
    'ggplot2' for visualization.

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>.