- Update to version 2.5.0
* Required Octave version number has been raised to 3.6.0.
* Sequential design of experiments:
- `stk_sampcrit_akg_eval`: New function. Computes the
Approximate Knowledge Gradient (AKG) sampling criterion.
- `stk_example_doe05`: Example script that demonstrates the
use of the AKG and EQI sampling criteria for noisy
optimization.
- New (experimental) classes to represent sampling criterion
objects: `stk_sampcrit_ei`, `stk_sampcrit_akg`,
`stk_sampcrit_eqi`.
- `stk_sampcrit_ei_eval`: Remove deprecated calling forms.
* Design of experiments:
- `stk_factorialdesign`: Accept one-variable `stk_dataframe`
objects as factors and preserve column names in this case.
- `stk_sampling_nesteddesign`: New function to create nested
designs.
- `stk_sampling_nestedlhs`: New function to create nested LHS
(NLHS).
- `stk_sampling_sobol`: Fix help text.
* Validation of models:
- `stk_distrib_normal_crps`: New function to compare
observations and predictive (Gaussian) density.
- Calling `stk_predict_leaveoneout` with no output arguments
now automatically creates two cross-validation diagnostics
in two subplots: prediction VS observations (left panel) and
normalized residuals (right panel).
- `stk_predict_leaveoneout` uses now the virtual LOO formula.
* `stk_dataframe` and related classes
- `stk_hrect`: Preserve column names for `stk_dataframe`