Motivation | Key parameters | One-factor augmentation | Step 1: compute the candidate region | Step 2: choose a point and augment | Comparing efficiency before and after | Using the optimal design as reference (calc_optimal_design = TRUE) | Two-factor augmentation | Initial design and candidate region | Three-factor augmentation | Augmenting with Ds-Optimality | Interactive mode
Package: optedr 3.0.1
Carlos de la Calle-Arroyo
optedr: Calculating Optimal and D-Augmented Designs for Single- and Multi-Factor Models
Calculates D-, Ds-, A-, I- and L-optimal designs, weighted combinations of these via a Compound criterion, and KL-optimal designs for model discrimination, for non-linear single- and multi-factor models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Multi-factor models use design variables x1, x2, … with a named-list design space; single-factor models remain backward compatible. Compares designs via their efficiency, augments any design with a controlled efficiency loss, and provides efficient rounding functions to convert approximate designs to exact ones.
Authors:
optedr_3.0.1.tar.gz
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optedr_3.0.1.tgz(r-4.6-any)optedr_3.0.1.tgz(r-4.5-any)
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optedr_3.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
optedr/json (API)
| # Install 'optedr' in R: |
| install.packages('optedr', repos = c('https://kezrael.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kezrael/optedr/issues
Last updated from:f80e2bcc81. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 268 | ||
| source / vignettes | OK | 235 | ||
| linux-release-x86_64 | OK | 226 | ||
| macos-release-arm64 | OK | 161 | ||
| macos-oldrel-arm64 | OK | 166 | ||
| windows-devel | OK | 217 | ||
| windows-release | OK | 214 | ||
| windows-oldrel | OK | 215 | ||
| wasm-release | OK | 177 |
Exports:augment_designcombinatorial_rounddesign_efficiencyefficient_roundget_augment_regionmake_glm_familymake_kl_funopt_desshiny_augmentshiny_optimal
Dependencies:base64encbslibcachemclicommonmarkcpp11crayondigestfarverfastmapfontawesomefsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlifecyclemagrittrmemoisemimeotelpromisespurrrR6rappdirsRColorBrewerRcpprlangS7sassscalesshinysourcetoolsvctrsviridisLitewithrxtable
Augmenting designs with controlled efficiency loss
Last update: 2026-06-22
Started: 2026-06-22
Introduction to optedr: optimal designs for non-linear models
The optimal design problem | A first example: D-Optimality in one factor | Other optimality criteria | Ds-Optimality | A-Optimality | I-Optimality | L-Optimality | Multi-dimensional design spaces | Two-factor model: bisubstrate Michaelis-Menten | Three factors and beyond | Compound criterion | Design efficiency | Rounding to exact designs | efficient_round() | combinatorial_round()
Last update: 2026-06-22
Started: 2026-06-22
KL-Optimality: designs for model discrimination
Background | API overview | Example 1: Michaelis-Menten vs linear rival (Normal, 1D) | Example 2: exponential decay, Poisson family | Using make_kl_fun() for custom KL functions | Supported pairs | Example 3: Normal with different variances (1D) | Example 4: two-factor MM vs linear rival (make_kl_fun, 2D) | Example 5: discriminating error structures — the Hill model
Last update: 2026-06-22
Started: 2026-06-22
