'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This package is the 'DataSHIELD' interface implementation to analyze data
    shared on a 'MOLGENIS Armadillo' server. 'MOLGENIS Armadillo' is a
    light-weight 'DataSHIELD' server using a file store and an 'RServe' server.

A collection of functions to search and download 
    Digital Surface Model (DSM) and Light Detection and Ranging (LiDAR) data via APIs, 
    including 'OpenTopography' <https://portal.opentopography.org/apidocs/> and 
    'TNMAccess' <https://apps.nationalmap.gov/tnmaccess/#/>, and canopy tree height data.

'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This package is the 'DataSHIELD' interface implementation for 'Opal', which is
    the data integration application for biobanks by 'OBiBa'. Participant data, once
    collected from any data source, must be integrated and stored in a central
    data repository under a uniform model. 'Opal' is such a central repository.
    It can import, process, validate, query, analyze, report, and export data.
    'Opal' is the reference implementation of the 'DataSHIELD' infrastructure.

Test for no adverse shift in two-sample comparison when we
    have a training set, the reference distribution, and a test set. The
    approach is flexible and relies on a robust and powerful test
    statistic, the weighted AUC. Technical details are in Kamulete, V. M.
    (2021) <arXiv:1908.04000>. Modern notions of outlyingness such as
    trust scores and prediction uncertainty can be used as the underlying
    scores for example.

Provides efficient Markov chain Monte Carlo (MCMC) algorithms for 
    dynamic shrinkage processes, which extend global-local shrinkage priors to 
    the time series setting by allowing shrinkage to depend on its own past. 
    These priors yield locally adaptive estimates, useful for time series and 
    regression functions with irregular features. The package includes full MCMC 
    implementations for trend filtering using dynamic shrinkage on signal differences, 
    producing locally constant or linear fits with adaptive credible bands. 
    Also included are models with static shrinkage and normal-inverse-Gamma priors for comparison. 
    Additional tools cover dynamic regression with time-varying coefficients and 
    B-spline models with shrinkage on basis differences, allowing for flexible 
    curve-fitting with unequally spaced data. Some support for heteroscedastic errors, 
    outlier detection, and change point estimation.
    Methods in this package are described in Kowal et al. (2019) <doi:10.1111/rssb.12325>, 
    Wu et al. (2024) <doi:10.1080/07350015.2024.2362269>, Schafer and Matteson (2024)
    <doi:10.1080/00401706.2024.2407316>, and Cho and Matteson (2024) <doi:10.48550/arXiv.2408.11315>.