The package ships the results of its publication-grade simulation panel as compressed `.rds` files under `inst/extdata/`. The vignettes read these so they never re-run a simulation at build time. Regenerate them with the scripts in `data-raw/` (see `data-raw/run_all_data.R`).
Value
If `name` is `NULL`, a tibble of available datasets. Otherwise the `list(results, meta)` bundle for that dataset, or `NULL` with a message if it has not been generated yet.
Details
Available datasets (name -> contents):
- `repro_benchmarks`, `repro_summary`
Benchmark reproduction on the five networks vs Louvain/Leiden and literature best-known Q.
- `doe_screening`, `doe_rsm`
Design-of-experiments runs: factorial screening and a central composite design in `(alpha_c, alpha_s)`.
- `lfr_robustness`
Q and ARI across the LFR-like mixing sweep.
- `prop6_summary`, `prop6_trajectories`
Reactive-update concentration.
- `pool_sensitivity`
`(m, y, B)` grid for LCDA-GR.
- `vnmi_nprime`
VNMI candidate-subset size `n'` sweep: modularity and local-search time vs `n'` across density regimes.
- `degeneracy`
Near-best partition counts and pairwise NMI.
- `eda_replicates`
Replicated `(Q, H)` for exploratory analysis.
- `nce_alternatives`
Global vs community-conditioned NCE leaders.
- `lcda_ecg`, `overlap_lcda_ecg`, `stability_pool`, `consensus_leader_test`
LCDA-ECG ensemble consensus: LFR recovery, overlapping/bridge nodes, pool-stability stopping rule, and the consensus-vs-central leader comparison.
- `largescale`
Recovery and runtime up to n = 5e4 (synthetic LFR).
- `realnet_amazon`, `realnet_coauthor`
Large real networks with ground truth: modularity Q and recovery (NMI/ARI vs labels) on Amazon-Computers (co-purchase) and Coauthor-Physics (co-authorship).
- `openalex_leaders`
Leader validation against an external citation signal on an OpenAlex co-authorship graph.
- `gnn_baseline`
Graph-auto-encoder baseline (true-k and auto-k) vs classical methods on LFR.
- `weighted_demo`
Weighted-graph demonstration (weights aid recovery).
- `blogs_table9`, `blogs_timing`
Weighted Political Blogs reproduction (mean/max Q, timings).
- `doe_lfr_recovery`
DoE screening/RSM scored by LFR recovery.
- `leader_utility`, `leader_vs_twostage`, `leader_vs_twostage_lfr`
Downstream leader utility (IC/LT spread, coverage) vs two-stage detect-then-centrality pipelines.
Examples
lcda_data() # list what is available
#> # A tibble: 28 × 1
#> dataset
#> <chr>
#> 1 blogs_table9
#> 2 blogs_timing
#> 3 consensus_leader_test
#> 4 degeneracy
#> 5 doe_lfr_recovery
#> 6 doe_rsm
#> 7 doe_screening
#> 8 eda_replicates
#> 9 gnn_baseline
#> 10 largescale
#> # ℹ 18 more rows
if (FALSE) { # \dontrun{
d <- lcda_data("repro_summary")
d$meta$limitations
head(d$results)
} # }