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1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def center(x: MatriD, mu_x: VectoD): MatriD

   Center the input matrix 'x' to zero mean, column-wise, by subtracting the mean.

   Center the input matrix 'x' to zero mean, column-wise, by subtracting the mean.

   x

   the input matrix to center

   mu_x

   the vector of column means of matrix x

6.  def clone(): AnyRef

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7.  def corr(x: MatrixD): MatrixD

   Return the correlation matrix for the columns of matrix 'x'.

   Return the correlation matrix for the columns of matrix 'x'. Note: sample vs. population results in essentailly the same values.

   x

   the matrix whose column-column correlations are sought

8.  def corrMat(y: VectorD): MatrixD

   Return the first-order auto-regressive correlation matrix for vector (e.g., time series) 'y'.

   Return the first-order auto-regressive correlation matrix for vector (e.g., time series) 'y'.

   y

   the vector whose auto-regressive correlation matrix is sought

   See also

   halweb.uc3m.es/esp/Personal/personas/durban/esp/web/notes/gls.pdf

9.  def cos(x: MatrixD): MatrixD

   Return the cosine similarity matrix for the columns of matrix 'x'.

   Return the cosine similarity matrix for the columns of matrix 'x'.

   x

   the matrix whose column-column cosines are sought

   See also

   stats.stackexchange.com/questions/97051/building-the-connection-between-cosine-similarity-and-correlation-in-r

10.  def cov(x: MatrixD): MatrixD

    Return the sample covariance matrix for the columns of matrix 'x'.

    Return the sample covariance matrix for the columns of matrix 'x'.

    x

    the matrix whose column covariances are sought

11.  def covMat(y: VectorD): MatrixD

    Return the first-order auto-regressive covariance matrix for vector (e.g., time series) 'y'.

    Return the first-order auto-regressive covariance matrix for vector (e.g., time series) 'y'.

    y

    the vector whose auto-regressive covariance matrix is sought

    See also

    halweb.uc3m.es/esp/Personal/personas/durban/esp/web/notes/gls.pdf

12.  final def eq(arg0: AnyRef): Boolean

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13.  def equals(arg0: Any): Boolean

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14.  def finalize(): Unit

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15.  final def getClass(): Class[_]

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16.  def hashCode(): Int

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17.  final def isInstanceOf[T0]: Boolean

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18.  final def ne(arg0: AnyRef): Boolean

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19.  final def notify(): Unit

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20.  final def notifyAll(): Unit

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21.  def pcov(x: MatrixD): MatrixD

    Return the population covariance matrix for the columns of matrix 'x'.

    Return the population covariance matrix for the columns of matrix 'x'.

    x

    the matrix whose column columns covariances are sought

22.  final def synchronized[T0](arg0: ⇒ T0): T0

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23.  def toString(): String

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24.  final def wait(): Unit

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25.  final def wait(arg0: Long, arg1: Int): Unit

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26.  final def wait(arg0: Long): Unit

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