Bindings to 'libarchive' <http://www.libarchive.org> the
    Multi-format archive and compression library. Offers R connections and
    direct extraction for many archive formats including 'tar', 'ZIP',
    '7-zip', 'RAR', 'CAB' and compression formats including 'gzip',
    'bzip2', 'compress', 'lzma' and 'xz'.

Scraping content from archived web pages stored in
    the 'Internet Archive' (<https://archive.org>) using a systematic
    workflow.  Get an overview of the mementos available from the
    respective homepage, retrieve the Urls and links of the page and
    finally scrape the content. The final output is stored in tibbles,
    which can be then easily used for further analysis.

Data exploration and modelling is a process in which a lot of data
    artifacts are produced. Artifacts like: subsets, data aggregates, plots,
    statistical models, different versions of data sets and different versions
    of results. The more projects we work with the more artifacts are produced
    and the harder it is to manage these artifacts. Archivist helps to store
    and manage artifacts created in R. Archivist allows you to store selected
    artifacts as a binary files together with their metadata and relations.
    Archivist allows to share artifacts with others, either through shared
    folder or github. Archivist allows to look for already created artifacts by
    using it's class, name, date of the creation or other properties. Makes it
    easy to restore such artifacts. Archivist allows to check if new artifact
    is the exact copy that was produced some time ago. That might be useful
    either for testing or caching.

The extension of the 'archivist' package integrating the archivist with GitHub via GitHub API, 'git2r' packages and 'httr' package.

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 (2019) <arXiv:1909.01836>.