STK_PREDICT_LEAVEONEOUT computes LOO predictions and residuals

 CALL: LOO_PRED = stk_predict_leaveoneout (M_PRIOR, XI, ZI)

    computes LOO predictions for (XI, ZI) using the prior model M_PRIOR.  The
    result is a dataframe with n rows and two columns, where n is the common
    number of rows of XI and ZI.  The first column is named 'mean' and contains
    LOO prediction means.  The second column is named 'var' and contains LOO
    prediction variances.

 CALL: [LOO_PRED, LOO_RES] = stk_predict_leaveoneout (M_PRIOR, XI, ZI)

    also returns LOO residuals.  The result LOO_RES is a dataframe with n rows
    and two columns.  The first column is named 'residuals' and contains raw
    (i.e., unnormalized) residuals.  The second column is named 'norm_res' and
    contains normalized residuals.

 CALL: [LOO_PRED, LOO_RES] = stk_predict_leaveoneout (M_POST)

    does the same as above using a posterior model object M_POST directly.

 CALL: stk_predict_leaveoneout (...)

    automatically produces LOO cross-validations plots in the current figure,
    using stk_plot_predvsobs (left panel) and stk_plot_histnormres (right
    panel).

 REMARK

    This function actually computes pseudo-LOO prediction and residuals,
    where the same parameter vector is used for all data points.

 See also stk_example_kb10, stk_plot_predvsobs, stk_plot_histnormres