tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of DoseFinding and gsDesign2 — release velocity, themes, recent moves, and the top alternatives to consider.
New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing
DoseFinding implements MCP-Mod and related dose-response methodology for clinical trial design and analysis. In 2024 it changed hands — Marius Thomas took over as maintainer, Novartis was recorded as copyright holder and funder, and the package moved to the openpharma GitHub organisation with roxygen documentation and a proper NEWS file. The two releases since have added substantive methodology: model averaging for dose-response fitting in 1.3-1, and conditional and predictive power for interim analyses in 1.4-1.
Group sequential design tooling that now monitors for harm, not just efficacy and futility.
gsDesign2 is the Merck-authored R package for group sequential clinical trial design under non-proportional hazards, and it has spent the last two years filling in the statistical surface its predecessor gsDesign established. Recent releases added conditional power (gs_cp, gs_cp_npe), sequential p-values, risk-difference designs with minimal risk weighting, and boundary updates from blinded interim estimates. Version 1.2.0 adds harm boundaries across the AHR and NPE design and power functions, wired through every summary and table export path.
DoseFinding implements MCP-Mod and related dose-response methodology for clinical trial design and analysis. In 2024 it changed hands — Marius Thomas took over as maintainer, Novartis was recorded as copyright holder and funder, and the package moved to the openpharma GitHub organisation with roxygen documentation and a proper NEWS file. The two releases since have added substantive methodology: model averaging for dose-response fitting in 1.3-1, and conditional and predictive power for interim analyses in 1.4-1.
The pattern before the handover was maintenance — R-devel compliance, a bug fix, a link. After it, each release carries a named methodological addition with an acknowledged contributor, plus documentation to match: a longitudinal analysis vignette shipped alongside the interim power work. Housekeeping continues underneath, mostly clearing deprecated ggplot2 interfaces, aes_string in one release and qplot in the next.
Given the last two releases each added one method with a supporting vignette, expect the next to follow the same shape. Both additions so far extend the package beyond fixed dose-response fitting, so adaptive and interim methodology is the more likely direction.
gsDesign2 is the Merck-authored R package for group sequential clinical trial design under non-proportional hazards, and it has spent the last two years filling in the statistical surface its predecessor gsDesign established. Recent releases added conditional power (gs_cp, gs_cp_npe), sequential p-values, risk-difference designs with minimal risk weighting, and boundary updates from blinded interim estimates. Version 1.2.0 adds harm boundaries across the AHR and NPE design and power functions, wired through every summary and table export path.
The package is converging on parity with gsDesign while extending past it — each release either closes a gap against the older package or adds a boundary type gsDesign never had. A visible second track is output plumbing: every new statistical feature now arrives already threaded through summary(), gs_bound_summary(), as_gt(), and as_rtf(), which is what regulatory submission work actually consumes. Performance work is steady but secondary, with gs_design_ahr() roughly 2x faster in 1.1.9.
Expect the harm boundary work to propagate into the WLR and risk-difference design families, which are the two design branches 1.2.0 left untouched, along with a vignette bridging harm boundaries to the remaining gsDesign test types.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either DoseFinding or gsDesign2.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all DoseFinding alternatives → · See all gsDesign2 alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — clinical-trials — within Analytics. gsDesign2 is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. gsDesign2 is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top DoseFinding alternatives in Analytics are ranked by recent ship velocity. Browse the "DoseFinding alternatives" section above for the current picks, or visit /alternatives/dosefinding for the full list with editorial commentary on each.
Top gsDesign2 alternatives in Analytics are ranked by recent ship velocity. Browse the "gsDesign2 alternatives" section above for the current picks, or visit /alternatives/gsdesign2 for the full list with editorial commentary on each.