checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
Namespaces in Imports field not imported from:
  ‘ggplot2’ ‘grDevices’ ‘knitr’ ‘utils’
  All declared Imports should be used.
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
Namespaces in Imports field not imported from:
  ‘ggplot2’ ‘grDevices’ ‘knitr’ ‘utils’
  All declared Imports should be used.
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^
checkRd: (-1) MVNtestchar-package.Rd:16-17: Lost braces; missing escapes or markup?
    16 | {Provides a test of multivariate normality of a sample which does not require estimation of the nuisance parameters, the mean      vector and covariance matrix.  Rather, a sequence of transformations removes these nuisance parameters, resulting in a set of    sample matrices that are positive definite.  If, and only if the original data is multivariate normal, these matrices are       uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle.  The package performs a goodness    of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the  support region    of positive definite matrices for p equals 2.
       | ^