Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>.

For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) <DOI:10.1137/19M1289893>. The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2022) <DOI:10.1111/rssc.12558>.

Implements cubic regularization methods (ARC) for local
    optimization problems common in statistics and applied research. Provides
    robust handling of ill-conditioned, nonconvex, and indefinite Hessian
    problems with automatic saddle point escape. Supports box constraints;
    linear equality constraints are planned for a future release.

Fast processing of ArcGIS FeatureCollection protocol buffers in R.
  It is designed to work seamlessly with 'httr2' and integrates with 'sf'.

Functions to efficiently query 'ArcGIS REST' APIs 
  <https://developers.arcgis.com/rest/>. 
  Both spatial and SQL queries can be used to retrieve data. 
  Simple Feature (sf) objects are utilized to perform spatial queries. 
  This package was neither produced nor is maintained by Esri.