# doc-cache created by Octave 5.1.0
# name: cache
# type: cell
# rows: 3
# columns: 14
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 15
stk_discretecov


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1013
 STK_DISCRETECOV computes a covariance matrix for a discrete model

 CALL: K = stk_discretecov (PARAM, X, Y)

    computes the covariance matrix K between the sets of locations X and
    Y, using the discrete model (a kriging model on a discrete space)
    parameters PARAM. The inputs arguments X and Y are expected to be
    vectors of indices. The output matrix K has size NX x NY, where NX is
    the length of X and NY the length of Y. PARAM must contain a field
    PARAM.K which is the full covariance matrix on the discrete input
    space.

 CALL: K = stk_discretecov (PARAM, X, Y, DIFF, PAIRWISE)

    computes the covariance *vector* between the sets of locations X and
    Y if PAIRWISE is TRUE, and the covariance *matrix* otherwise. In the
    first case, the output K is a vector of length N, where N is the
    common number of rows of X and Y. In the second, it is an NX x NY
    matrix as above. The argument DIFF has no effect, but must be equal
    to -1 since this is not a parametric model.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 66
 STK_DISCRETECOV computes a covariance matrix for a discrete model



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 16
stk_expcov_aniso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1207
 STK_EXPCOV_ANISO computes the anisotropic exponential covariance function

 CALL: K = stk_expcov_aniso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and Y,
   using the exponential covariance function with parameters PARAM. The output
   matrix K has size NX x NY, where NX is the number of rows in X and NY the
   number of rows in Y. The vector of parameters must have DIM + 1 elements,
   where DIM is the common number of columns of X and Y:

     * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

     * PARAM(1+i) = - log (RHO(i)), where RHO(i) is the range parameter
       for the ith dimension.

 CALL: dK = stk_expcov_aniso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_aniso
   (PARAM, X, Y)).

 CALL: K = stk_expcov_aniso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y.  The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 74
 STK_EXPCOV_ANISO computes the anisotropic exponential covariance function



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 14
stk_expcov_iso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1110
 STK_EXPCOV_ISO computes the isotropic exponential covariance function

 CALL: K = stk_expcov_iso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and
   Y, using the isotropic exponential covariance function with parameters
   PARAM. The output matrix K has size NX x NY, where NX is the number of rows
   in X and NY the number of rows in Y. The vector of parameters must have two
   elements:

     * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

     * PARAM(2) = - log (RHO), where RHO is the range parameter.

 CALL: dK = stk_expcov_iso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_iso
   (PARAM, X, Y)).

 CALL: K = stk_expcov_iso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y.  The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 70
 STK_EXPCOV_ISO computes the isotropic exponential covariance function



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 18
stk_gausscov_aniso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1279
 STK_GAUSSCOV_ANISO computes the anisotropic Gaussian covariance function

 CALL: K = stk_gausscov_aniso (PARAM, X, Y)

   computes  the covariance matrix K  between  the sets of locations  X  and Y,
   using  the anisotropic Gaussian covariance  function  with parameters PARAM.
   The output matrix K has size  NX x NY,  where NX is the number of rows  in X
   and NY  the number of rows in Y.  The vector of parameters must have DIM + 1
   elements, where DIM is the common number of columns of X and Y:

     * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

     * PARAM(2:END) = - log (RHO), where RHO is the vector of range parameters.

 CALL: dK = stk_gausscov_aniso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to PARAM(DIFF)
   if DIFF is between 1 and DIM + 1, or the covariance matrix itself if DIFF is
   equal to -1  (in which case this is equivalent to stk_gausscov_aniso (PARAM,
   X, Y)).

 CALL: K = stk_gausscov_aniso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector  (or a derivative of it if DIFF > 0)  between
   the sets of locations X and Y.  The output K is a vector of length N,  where
   N is the common number of rows of X and Y.

 See also: stk_rbf_gauss, stk_gausscov_iso



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 73
 STK_GAUSSCOV_ANISO computes the anisotropic Gaussian covariance function



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 16
stk_gausscov_iso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1174
 STK_GAUSSCOV_ISO computes the isotropic Gaussian covariance function

 CALL: K = stk_gausscov_iso (PARAM, X, Y)

   computes  the covariance matrix K  between  the sets of locations  X  and Y,
   using the isotropic Gaussian covariance function with parameters PARAM.  The
   output matrix K has size NX x NY, where NX is the number of rows in X and NY
   the number of rows in Y. The vector of parameters must have two elements:

     * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

     * PARAM(2) = - log (RHO), where RHO is the range parameter.

 CALL: dK = stk_gausscov_iso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to PARAM(DIFF)
   if DIFF is equal to 1 or 2, or the covariance matrix itself if DIFF is equal
   to -1 (in which case this is equivalent to stk_gausscov_iso (PARAM, X, Y)).

 CALL: K = stk_gausscov_iso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector  (or a derivative of it if DIFF > 0)  between
   the sets of locations X and Y.  The output K is a vector of length N,  where
   N is the common number of rows of X and Y.

 See also: stk_rbf_gauss, stk_gausscov_aniso



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 69
 STK_GAUSSCOV_ISO computes the isotropic Gaussian covariance function



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 21
stk_materncov32_aniso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1246
 STK_MATERNCOV32_ANISO computes the anisotropic Matern covariance with nu=3/2

 CALL: K = stk_materncov32_aniso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and Y,
   using the anisotropic Matern covariance function with nu=3/2 and
   parameters PARAM. The output matrix K has size NX x NY, where NX is the
   number of rows in X and NY the number of rows in Y. The vector of
   parameters must have DIM + 1 elements, where DIM is the common number
   of columns of X and Y:

     * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

     * PARAM(1+i) = - log (RHO(i)), where RHO(i) is the range parameter
       for the ith dimension.

 CALL: dK = stk_materncov32_aniso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_aniso
   (PARAM, X, Y)).

 CALL: K = stk_materncov32_aniso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y.  The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 77
 STK_MATERNCOV32_ANISO computes the anisotropic Matern covariance with nu=3/2



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 19
stk_materncov32_iso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1134
 STK_MATERNCOV32_ISO computes the isotropic Matern covariance with nu=3/2

 CALL: K = stk_materncov32_iso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and
   Y, using the isotropic Matern covariance function with nu=3/2 and
   parameters PARAM. The output matrix K has size NX x NY, where NX is
   the number of rows in X and NY the number of rows in Y. The vector of
   parameters must have two elements:

     * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

     * PARAM(2) = - log (RHO), where RHO is the range parameter.

 CALL: dK = stk_materncov32_iso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_iso
   (PARAM, X, Y)).

 CALL: K = stk_materncov32_iso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y.  The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 73
 STK_MATERNCOV32_ISO computes the isotropic Matern covariance with nu=3/2



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 21
stk_materncov52_aniso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1243
 STK_MATERNCOV52_ANISO computes the anisotropic Matern covariance with nu=5/2

 CALL: K = stk_materncov52_aniso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and Y,
   using the anisotropic Matern covariance function with nu=5/2 and
   parameters PARAM. The output matrix K has size NX x NY, where NX is the
   number of rows in X and NY the number of rows in Y. The vector of
   parameters must have DIM + 1 elements, where DIM is the common number
   of columns of X and Y:

    * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

    * PARAM(1+i) = - log (RHO(i)), where RHO(i) is the range parameter for
      the i-th dimension.

 CALL: dK = stk_materncov52_aniso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_aniso
   (PARAM, X, Y)).

 CALL: K = stk_materncov52_aniso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y. The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 77
 STK_MATERNCOV52_ANISO computes the anisotropic Matern covariance with nu=5/2



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 19
stk_materncov52_iso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1119
 STK_MATERNCOV52_ISO computes the isotropic Matern covariance

 CALL: K = stk_materncov52_iso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and Y,
   using the isotropic Matern covariance function with nu=5/2 and parameters
   PARAM. The output matrix K has size NX x NY, where NX is the number of
   rows in X and NY the number of rows in Y. The vector of parameters must
   have two elements:

    * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

    * PARAM(2) = - log (RHO), where RHO is the range parameter.

 CALL: dK = stk_materncov52_iso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_iso
   (PARAM, X, Y)).

 CALL: K = stk_materncov52_iso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y. The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 61
 STK_MATERNCOV52_ISO computes the isotropic Matern covariance



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 19
stk_materncov_aniso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1273
 STK_MATERNCOV_ANISO computes the anisotropic Matern covariance

 CALL: K = stk_materncov_aniso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and Y,
   using the anisotropic Matern covariance function with parameters PARAM.
   The output matrix K has size NX x NY, where NX is the number of rows in
   X and NY the number of rows in Y. The vector of parameters must have
   DIM + 1 elements, where DIM is the common number of columns of X and Y:

    * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

    * PARAM(2) = log(NU), where NU is the regularity parameter,

    * PARAM(2+i) = - log (RHO(i)), where RHO(i) is the range parameter for
      the ith dimension.

 CALL: dK = stk_materncov_aniso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_aniso
   (PARAM, X, Y)).

 CALL: K = stk_materncov_aniso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y. The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 63
 STK_MATERNCOV_ANISO computes the anisotropic Matern covariance



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 17
stk_materncov_iso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1167
 STK_MATERNCOV_ISO computes the isotropic Matern covariance

 CALL: K = stk_materncov_iso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and Y,
   using the isotropic Matern covariance function with parameters PARAM. The
   output matrix K has size NX x NY, where NX is the number of rows in X
   and NY the number of rows in Y. The vector of parameters must have
   three elements:

    * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

    * PARAM(2) = log(NU), where NU is the regularity parameter,

    * PARAM(3) = - log (RHO), where RHO is the range parameter.

 CALL: dK = stk_materncov_iso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_iso
   (PARAM, X, Y)).

 CALL: K = stk_materncov_iso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y. The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 59
 STK_MATERNCOV_ISO computes the isotropic Matern covariance



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 12
stk_noisecov


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 27
 STK_NOISECOV [deprecated]



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 27
 STK_NOISECOV [deprecated]




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 16
stk_sphcov_aniso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1215
 STK_SPHCOV_ANISO computes the anisotropic spherical covariance function

 CALL: K = stk_sphcov_aniso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and Y,
   using the anisotropic spherical covariance function with parameters PARAM.
   The output matrix K has size NX x NY, where NX is the number of rows in X
   and NY the number of rows in Y. The vector of parameters must have DIM + 1
   elements, where DIM is the common number of columns of X and Y:

     * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

     * PARAM(1+i) = - log (RHO(i)), where RHO(i) is the range parameter
       for the ith dimension.

 CALL: dK = stk_sphcov_aniso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_aniso
   (PARAM, X, Y)).

 CALL: K = stk_sphcov_aniso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y.  The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 72
 STK_SPHCOV_ANISO computes the anisotropic spherical covariance function



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 14
stk_sphcov_iso


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1106
 STK_SPHCOV_ISO computes the isotropic spherical covariance function

 CALL: K = stk_sphcov_iso (PARAM, X, Y)

   computes the covariance matrix K between the sets of locations X and
   Y, using the isotropic spherical covariance function with parameters PARAM.
   The output matrix K has size NX x NY, where NX is the number of rows in X
   and NY the number of rows in Y. The vector of parameters must have two
   elements:

     * PARAM(1) = log (SIGMA ^ 2), where SIGMA is the standard deviation,

     * PARAM(2) = - log (RHO), where RHO is the range parameter.

 CALL: dK = stk_sphcov_iso (PARAM, X, Y, DIFF)

   computes the derivative of the covariance matrix with respect to
   PARAM(DIFF) if DIFF~= -1, or the covariance matrix itself if DIFF is
   equal to -1 (in which case this is equivalent to stk_materncov_iso
   (PARAM, X, Y)).

 CALL: K = stk_sphcov_iso (PARAM, X, Y, DIFF, PAIRWISE)

   computes the covariance vector (or a derivative of it if DIFF > 0)
   between the sets of locations X and Y.  The output K is a vector of
   length N, where N is the common number of rows of X and Y.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 68
 STK_SPHCOV_ISO computes the isotropic spherical covariance function





