Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination.

We propose a method to estimate the probability of an 
    undetected case of COVID-19 in a defined setting, when a given number of 
    people have been exposed, with a given pretest probability of having 
    COVID-19 as a result of that exposure. Since we are interested in undetected
    COVID-19, we assume no person has developed symptoms (which would warrant 
    further investigation) and that everyone was tested on a given day, and all 
    tested negative.

The COVID Symptom Study is a non-commercial project that uses
    a free mobile app to facilitate real-time data collection of symptoms,
    exposures, and risk factors related to COVID19. The package allows
    easy access to summary statistics data from COVID Symptom Study
    Sweden.

Matrix-variate covariance estimation via the Kronecker-core decomposition. Computes the Kronecker and core covariance matrices corresponding to an arbitrary covariance matrix, and provides an empirical Bayes covariance estimator that adaptively shrinks towards the space of separable covariance matrices. For details, see Hoff, McCormack and Zhang (2022) <arXiv:2207.12484> "Core Shrinkage Covariance Estimation for Matrix-variate data".

Track and report code coverage for your package and (optionally)
    upload the results to a coverage service like 'Codecov' <https://about.codecov.io> or
    'Coveralls' <https://coveralls.io>. Code coverage is a measure of the amount of
    code being exercised by a set of tests. It is an indirect measure of test
    quality and completeness. This package is compatible with any testing
    methodology or framework and tracks coverage of both R code and compiled
    C/C++/FORTRAN code.

Covariance Regression with Random Forests (CovRegRF) is a
    random forest method for estimating the covariance matrix of a
    multivariate response given a set of covariates. Random forest trees
    are built with a new splitting rule which is designed to maximize the
    distance between the sample covariance matrix estimates of the child
    nodes. The method is described in Alakus et al. (2023)
    <doi:10.1186/s12859-023-05377-y>. 'CovRegRF' uses 'randomForestSRC' package
    (Ishwaran and Kogalur, 2022) 
    <https://cran.r-project.org/package=randomForestSRC> by freezing at the
    version 3.1.0. The custom splitting rule feature is utilised to apply the
    proposed splitting rule. The 'randomForestSRC' package implements 'OpenMP' 
    by default, contingent upon the support provided by the target architecture 
    and operating system. In this package, 'LAPACK' and 'BLAS' libraries are 
    used for matrix decompositions.