opt_des() now accepts
criterion = "KL-Optimality" to find designs that best
discriminate between a reference model and a rival model, maximising the
Kullback-Leibler divergence between them (T-optimality generalised to
exponential-family GLMs).make_kl_fun() builds the point KL-divergence function
kl_fun(x, beta2) from two model/family specifications,
supporting "Normal", "Poisson",
"Binomial" and "Gamma" families, including
cross-dispersion Normal vs. Normal and Gamma vs. Gamma pairs. Other
family pairs can be supplied via a user-written
kl_fun.rival_pars,
rival_lower, rival_upper); the cocktail
algorithm then finds the design maximising it.design_efficiency() and summary.optdes()
support KL-Optimality results, reporting the family/model information or
“user-supplied” for a custom kl_fun.opt_des() now accepts
criterion = "Compound" with a compound
argument: a list of two or more per-criterion specifications, each with
criterion and weight fields (plus
criterion-specific parameters: par_int for Ds,
reg_int for I, matB for L). Weights are
normalised automatically.d_c(x) = sum_i w_i * d_i(x) is a valid convex combination;
the cocktail algorithm converges via the same multiplicative weight
update used for I/A/L-Optimality.print.optdes and summary.optdes show the
compound composition (weights and sub-criteria) when
criterion = "Compound".design_efficiency() computes compound efficiency as
phi_c(M*) / phi_c(M_xi) where
phi_c = sum_i w_i * phi_i.get_augment_region() now returns an
"augment_region" S3 object instead of a plain numeric
vector. The crosspoints vector is accessible via
region$region; multi-factor calls return a candidate data
frame there. A print method summarises intervals (1D) or
candidate count and efficiency range (multi-factor).opt_des() now accepts multi-factor models using the
naming convention x1, x2, … for design
variables and design_space as a named list,
e.g. list(x1 = c(0, 10), x2 = c(0, 5)). Single-factor
models using x are fully backward compatible.d = 2 the sensitivity plot is a viridis
heatmap with the Equivalence Theorem contour and support-point labels
overlaid.design_efficiency() accepts designs with
x1, x2, … column names in addition to the
legacy Point / Weight format.augment_design() and get_augment_region()
support multi-factor models for all five criteria (D, Ds, A, I, L). For
d = 2 the candidate region is shown as a plasma heatmap
with a white contour at delta_val; for any d a
sample of candidate points is printed. Interactive
coordinate-by-coordinate prompting works analogously to the 1D readline
workflow.efficient_round() and
combinatorial_round() accept any data frame with a
Weight column, including multi-factor designs with
x1, x2, … columns.opt_des() and
get_augment_region() / augment_design()
accepts reg_int as a named list for multi-factor
models.DsWFMult and IWFMult: the
sensitivity plot was built from the stale closure
sensDs/sensI (last inner-loop iteration)
instead of the final recomputed sensM. Caused
Inf/NaN values in A/I/L-optimality
heatmaps.geom_contour inheriting fill
aesthetic from the global aes, producing a spurious warning
in 2D sensitivity plots.gradient22(): hardcoded "x" replaced
by detect_design_vars(), enabling Ds-Optimality augment for
multi-factor models.join_thresh heuristic: changed from
mean(ranges)/10 to min(ranges)/10 so designs
with heterogeneous factor scales (e.g. MPa vs K) converge
correctly.DWFMult where the inner weight loop
stopped after a single iteration due to a misplaced <
operator inside max().error_msg variable in
design_efficiency().weight_fun/grad ordering in
opt_des() when distribution is specified
(gradient was computed before the weight function was updated).shiny_optimal() error message for
missing markdown.augment_design() and get_augment_region()
gain delta_val and new_points parameters
enabling fully non-interactive / programmatic use.
new_points is validated for structure, positive weights,
design-space membership, and candidate-region membership.opt_des() gains max_iter parameter
(default 21) controlling the number of outer iterations of the cocktail
algorithm.efficient_round() gains a seed parameter
for reproducible tie-breaking.combinatorial_round() gains max_support
(default 15) and ask parameters. When the design exceeds
max_support support points the function shows an estimated
computation time and prompts for confirmation in interactive sessions,
or stops with an informative error in non-interactive ones.
ask = FALSE bypasses the check.solve() replaced throughout by inv_spd(),
a Cholesky-based stable inverse with a Moore-Penrose pseudoinverse
fallback and an informative warning that diagnoses the number of
non-identifiable parameters.inf_mat() vectorised using vapply +
crossprod (LAPACK DSYRK).crosspoints() rewritten with sign-change detection +
uniroot; removes 10 000 sequential nleqslv
calls. nleqslv moved from Imports to Suggests.update_weightsDS() now normalises weights to sum to
1.update_weights*() functions stop with an informative
error when weights become non-finite (diagnosis: model parameter
redundancy).result$atwood is now a documented list element (was a
hidden attribute).size aesthetic in line/segment geoms replaced
by linewidth.shiny_optimal() and shiny_augment() emit
an informative message with the web URL when called outside an
interactive session; check all required packages simultaneously and list
every missing one in a single error.Removed dependency from package “nloptr” as it’s set to be archived
opt_des now calculates designs for different
probability distributions, included some defaultsget_augment_regioncombinatorial_round.design_efficiency
to be in line with theory conventions.
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