Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/coroICA/>.

Manipulate and view coronavirus data and other societally relevant data at a basic level.

Offers access to a database on government responses to the COVID-19 pandemic. 
    To date, the 'CoronaNet' dataset provides the most comprehensive and granular 
    documentation of such government policies in the world, capturing data for 
    20 broad policy categories alongside many other dimensions, including the 
    initiator, target, and timing of a policy. This package is a programmatic 
    front-end to up-to-date 'CoronaNet' policy records and the 'CoronaNet' policy intensity index scores. 
    For more information, see Cheng et al. (2020) <doi:10.1038/s41562-020-0909-7>.

Provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus <https://systems.jhu.edu/research/public-health/ncov/>.

Implements a James-Stein-type shrinkage estimator for 
  the covariance matrix, with separate shrinkage for variances and correlations.  
  The details of the method are explained in Schafer and Strimmer (2005) 
  <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) 
  <DOI:10.2202/1544-6115.1252>.  The approach is both computationally as well
  as statistically very efficient, it is applicable to "small n, large p" data, 
  and always returns a positive definite and well-conditioned covariance matrix.  
  In addition to inferring the covariance matrix the package also provides 
  shrinkage estimators for partial correlations and partial variances.  
  The inverse of the covariance and correlation matrix 
  can be efficiently computed, as well as any arbitrary power of the 
  shrinkage correlation matrix.  Furthermore, functions are available for fast 
  singular value decomposition, for computing the pseudoinverse, and for 
  checking the rank and positive definiteness of a matrix.