STK_CONDITIONING produces conditioned sample paths

 CALL: ZSIMC = stk_conditioning (LAMBDA, ZI, ZSIM, XI_IND)

    produces conditioned sample paths ZSMIC from the unconditioned sample paths
    ZSIM, using the matrix of kriging weights LAMBDA. Conditioning is done with
    respect to a finite number NI of observations, located at the indices given
    in XI_IND (vector of length NI), with corresponding noiseless observed
    values ZI.

    The matrix LAMBDA must be of size NI x N, where N is the number of
    evaluation points for the sample paths; such a matrix is typically provided
    by stk_predict().

    Both ZSIM and ZSIMC have size N x NB_PATHS, where NB_PATH is the number
    sample paths to be dealt with. ZI is a column of length NI.

 CALL: ZSIMC = stk_conditioning (LAMBDA, ZI, ZSIM)

    assumes that the oberved values ZI correspond to the first NI evaluation
    points.

 CALL: ZSIMC = stk_conditioning (LAMBDA, ZI, ZSIM, XI_IND, NOISE_SIM)

    produces conditioned sample paths ZSMIC from the unconditioned sample paths
    ZSIM, using the matrix of kriging weights LAMBDA. Conditioning is done with
    respect to a finite number NI of observations, located at the indices given
    in XI_IND (vector of length NI), with corresponding noisy observed values
    ZI, using a NI x N matrix NOISE_SIM of simulated noise values.

 NOTE: Conditioning by kriging

    stk_conditioning uses the technique called "conditioning by kriging"
    (see, e.g., Chiles and Delfiner, Geostatistics: Modeling Spatial
    Uncertainty, Wiley, 1999)

 NOTE: Output type

    The output argument ZSIMC will be an stk_dataframe if either LAMBDA or ZSIM
    are stk_dataframe. In case of conflicting row names (coming from
    ZSIM.rownames on the one hand and LAMBDA.colnames on the other hand),
    ZSIMC.rownames is {}.

 EXAMPLE: stk_example_kb05

 See also stk_generate_samplepaths, stk_predict