Provides seamless access to the WEkEO Harmonised Data Access (HDA) API, enabling 
  users to query, download, and process data efficiently from the HDA platform. 
  With 'hdar', researchers and data scientists can integrate the extensive HDA 
  datasets into their R workflows, enhancing their data analysis capabilities. 
  Comprehensive information on the API functionality and usage is available 
  at <https://gateway.prod.wekeo2.eu/hda-broker/docs>.

User-friendly functions for leveraging (multiple) historical data set(s) for generalized
    linear models. The package contains functions for sampling from the posterior distribution of a
    generalized linear model using the prior induced by the Bayesian hierarchical model, power prior 
    by Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>, normalized power prior by Duan et al. (2006) 
    <doi:10.1002/env.752>, normalized asymptotic power prior by Ibrahim et al. (2015) <doi:10.1002/sim.6728>, 
    commensurate prior by Hobbs et al. (2011) <doi:10.1111/j.1541-0420.2011.01564.x>, robust meta-analytic-predictive 
    prior by Schmidli et al. (2014) <doi:10.1111/biom.12242>, the latent exchangeability prior by Alt et al. (2023) 
    <doi:10.48550/arXiv.2303.05223>, and a normal (or half-normal) prior. Functions for computing the marginal 
    log-likelihood under each of the implemented priors are also included. The package compiles all 
    the 'CmdStan' models once during installation using the 'instantiate' package.

Functions implementing change point detection methods using the maximum pairwise Bayes factor approach.
    Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques.

Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.

Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.