Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.

Functions and datasets to support Smilde, Marini, Westerhuis and Liland (2025, ISBN: 978-1-394-21121-0) 
   "Analysis of Variance for High-Dimensional Data - Applications in Life, Food and Chemical Sciences". 
   This implements and imports a  collection of methods for HD-ANOVA data analysis with common interfaces, result- and plotting 
   functions, multiple real data sets and four vignettes covering a range different applications.

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) in Bayesian analysis of generalized 
    linear models (GLMs) and survival models, along with support for Bayesian model averaging (BMA). The package provides 
    functions for sampling from posterior distributions under various informative priors, including 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>, latent exchangeability prior by Alt et al. (2024) 
    <doi:10.1093/biomtc/ujae083>, and a normal (or half-normal) prior. The package also includes functions for computing
    model averaging weights, such as BMA, pseudo-BMA, pseudo-BMA with the Bayesian bootstrap, and stacking (Yao et al.,
    2018 <doi:10.1214/17-BA1091>), as well as for generating posterior samples from the ensemble distributions to reflect 
    model uncertainty. In addition to GLMs, the package supports survival models including: (1) accelerated failure time 
    (AFT) models, (2) piecewise exponential (PWE) models, i.e., proportional hazards models with piecewise constant
    baseline hazards, and (3) mixture cure rate models that assume a common probability of cure across subjects, paired 
    with a PWE model for the non-cured population. Functions for computing marginal log-likelihoods under each implemented 
    prior are also included. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package.