STK_GENERATE_SAMPLEPATHS generates sample paths of a Gaussian process

 CALL: ZSIM = stk_generate_samplepaths (MODEL, XT)

    generates one sample path ZSIM of the Gaussian process MODEL discretized on
    the evaluation points XT.  The input argument XT can be either a numerical
    matrix or a dataframe.  The output argument ZSIM has the same number of
    rows as XT.  More precisely, on a factor space of dimension DIM,

     * XT must have size NS x DIM,
     * ZSIM will have size NS x 1,

    where NS is the number of simulation points.

    Note that, in the case where MODEL is a model for noisy observations, this
    function simulates sample paths of the underlying (latent) Gaussian
    process, i.e., noiseless observations.

 CALL: ZSIM = stk_generate_samplepaths (MODEL, XT, NB_PATHS)

    generates NB_PATHS sample paths at once.  In this case, the output argument
    ZSIM has size NS x NB_PATHS.

 CALL: ZSIM = stk_generate_samplepaths (MODEL, XI, ZI, XT)

    generates one sample path ZSIM, using the kriging model MODEL and the
    evaluation points XT, conditional on the evaluations (XI, ZI).

 CALL: ZSIM = stk_generate_samplepaths (MODEL, XI, ZI, XT, NB_PATHS)

    generates NB_PATHS conditional sample paths at once.

 NOTE: Sample size limitation

    This function generates (discretized) sample paths using a Cholesky
    factorization of the covariance matrix, and is therefore restricted to
    moderate values of the number of evaluation points.

 NOTE: Output type

    The output argument ZSIM is a plain (double precision) numerical array,
    even if XT is a data frame.  Row names can be added afterwards as follows:

       ZSIM = stk_generate_samplepaths (MODEL, XT);
       ZSIM = stk_dataframe (ZSIM, {}, XT.rownames);

 EXAMPLES: see stk_example_kb05, stk_example_kb07

 See also stk_conditioning, stk_cholcov