optedr 3.0.1

Bug fix

Fixed a test failure on CRAN’s ATLAS platform (R-devel, r90186): when a non-identifiable model causes the gradient to return NA/NaN, the criterion value dcrit() now returns NA instead of Inf under the ATLAS BLAS/LAPACK implementation. The previous trimming of the convergence vector (1L:(length(v) - sum(v == 0))) crashed on NA input. The vector is now truncated via seq_len(index) and a guard checks for any non-finite criterion value (NA, NaN, or Inf), throwing an informative “not identifiable” error consistently across all BLAS implementations and all optimality criteria (D, Ds, A, I, L, Compound).

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.
The inner adversarial optimisation searches for the rival parameters that minimise the integrated KL divergence (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.
The compound sensitivity function 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.
Any combination of D, Ds, A, I and L sub-criteria is supported, including three or more components.

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.
The cocktail algorithm dispatches to 1D or multi-factor paths transparently; for 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.
I-Optimality in opt_des() and get_augment_region() / augment_design() accepts reg_int as a named list for multi-factor models.

Fixed 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.
Fixed geom_contour inheriting fill aesthetic from the global aes, producing a spurious warning in 2D sensitivity plots.
Fixed gradient22(): hardcoded "x" replaced by detect_design_vars(), enabling Ds-Optimality augment for multi-factor models.
Fixed 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.
Fixed bug in DWFMult where the inner weight loop stopped after a single iteration due to a misplaced < operator inside max().
Fixed undefined error_msg variable in design_efficiency().
Fixed weight_fun/grad ordering in opt_des() when distribution is specified (gradient was computed before the weight function was updated).
Fixed wrong URL in 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).
All three cocktail algorithms check that the Atwood efficiency bound lies in [0, 100] and warn otherwise — the silent-failure signature of A/I/L-optimality applied to non-identifiable models.
result$atwood is now a documented list element (was a hidden attribute).
ggplot2 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

Added functionality to augment designs for Ds- and L-optimality.
opt_des now calculates designs for different probability distributions, included some defaults
Added function get_augment_region

Added functionality for a new rounding method, combinatorial_round.
Unified notation throughout functions.
Changed the orther of parameters in design_efficiency to be in line with theory conventions.

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