Subset and download data from EU Copernicus Climate Data Service:
    <https://cds.climate.copernicus.eu/>. Import information about the Earth's
    past, present and future climate from Copernicus into R without the need of
    external software.

The Copernicus Data Space Ecosystem, is an open
    ecosystem that provides free instant access to a wide range
    of data and services from the Copernicus Sentinel missions and
    more on our planet’s land, oceans and atmosphere. This package
    provides entry points to several APIs allowing users to access
    the data directly in R.

Copernicus Digital Elevation Model datasets (DEM) of 90 and 30 meters resolution using the 'awscli' command line tool. The Copernicus (DEM) is included in the Registry of Open Data on 'AWS (Amazon Web Services)' and represents the surface of the Earth including buildings, infrastructure and vegetation.

Subset and download data from EU Copernicus Marine
    Service Information: <https://data.marine.copernicus.eu>.
    Import data on the oceans physical and biogeochemical state
    from Copernicus into R without the need of external software.

Provides an R interface to the Copernicus Marine Service for downloading and accessing marine data. Integrates with the official 'copernicusmarine' Python library through 'reticulate'. Requires Python 3.7+ and a free Copernicus Marine account. See <https://marine.copernicus.eu/> and <https://pypi.org/project/copernicusmarine/> for more information.

A Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see   
     Manipur et al (2024, Nature Communications) <doi:10.1038/s41467-024-49990-8>.

Performs Bayesian nonparametric density estimation using Martingale
 posterior distributions including the Copula Resampling (CopRe) algorithm. 
 Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and
 an extension to include full uncertainty quantification via a predictive 
 sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate 
 random nonparametric distributions as output, leading to complete nonparametric 
 inference on posterior summaries. Routines for calculating arbitrary 
 functionals from the sampled distributions are included as well as an important 
 algorithm for finding the number and location of modes, which can then be used 
 to estimate the clusters in the data using, for example, k-means.
 Implements work developed in Moya B., Walker S. G. (2022).
 <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) 
 <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) 
 <doi:10.1080/01621459.1995.10476550>.