gganimate, ggplot2, gridExtra, qicharts2, reshape2
person(given = "Jo",
family = "van Schalkwyk",
role = c("aut", "cre"),
email = "jvanschalkwyk@gmail.com",
comment = c(ORCID = "0000-0002-0082-5243"))
Manipulate and view coronavirus data and other societally relevant data at a basic level.
Jo van Schalkwyk [aut, cre] (<https://orcid.org/0000-0002-0082-5243>)

The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset
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/>.
dplyr, knitr, rmarkdown, devtools, remotes, testthat (>=
2.1.0)

DT, dplyr, knitr, plotly, rmarkdown, remotes, testthat (>=
2.1.0)

c(person(given = "Rami",
family ="Krispin",
email = "rami.krispin@gmail.com",
role = c("aut", "cre")),
person(given = "Jarrett",
family = "Byrnes",
role = c("aut"),
email = "jarrett.byrnes@umb.edu",
comment = c(ORCID = "0000-0002-9791-9472")))
Rami Krispin [aut, cre],
Jarrett Byrnes [aut] (<https://orcid.org/0000-0002-9791-9472>)

DT, dplyr, knitr, plotly, readr, remotes, rmarkdown, testthat
(>= 2.1.0)

DT, dplyr, knitr, plotly, readr, rmarkdown, remotes, testthat
(>= 2.1.0)

Efficient Estimation of Covariance and (Partial) Correlation
Juliane Schaefer <schaefer@stat.uni-muenchen.de> and
Korbinian Strimmer <korbinian.strimmer@lmu.de>.
This package implements a shrinkage estimator to allow
the efficient inference of large-scale covariance matrices
from small sample data.  The resulting estimates are always
positive definite, more accurate than the empirical estimate,
well conditioned, computationally inexpensive, and require
only little a priori modeling.  The package also contains
similar functions for inferring correlations and partial
correlations.  In addition, it provides functions for fast svd
computation, for computing the pseuoinverse, and
for checking the rank and positive definiteness of a matrix.

Juliane Schaefer and Korbinian Strimmer.
This package implements a shrinkage estimator to allow
the efficient inference of large-scale covariance matrices
from small sample data.  The resulting estimates are always
positive definite, more accurate than the empirical estimate,
well conditioned, computationally inexpensive, and require
only little a priori modeling.  The package also contains
similar functions for inferring correlations and partial
correlations and partial covariances.  In addition, it provides
functions for fast svd computation, for computing the pseuoinverse,
for checking the rank and positive definiteness of a matrix, and
for the computationally fast inversion of the covariance matrix.