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brglm2 vs qtl2

A side-by-side editorial comparison of brglm2 and qtl2 — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

brglm2 vs qtl2: at a glance

Featurebrglm2qtl2
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesr-package, regression, bias-reduction, high-dimensionalqtl-mapping, statistical-genetics, bioinformatics, r-package
Last editorial update49m ago1h ago
WebsiteVisit →Visit →

What is brglm2?

A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression

brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.

Read the full brglm2 trajectory →

What is qtl2?

The standard QTL mapping package in R opened its genome scan to user-supplied likelihood models.

qtl2 is the R toolkit for QTL mapping in experimental crosses, covering genotype probability calculation, genome scans with and without polygenic effects, permutation testing, SNP association, and the plotting that goes with them. The last year of work has pushed hard in two directions: tooling for high-throughput expression and protein QTL studies, and a generalisation of the scan engine itself so the log-likelihood being maximised can be supplied by the user. Note that the release history reached this feed out of order, so feed position is not a reliable guide to which release came first.

Read the full qtl2 trajectory →

brglm2 vs qtl2: editorial side-by-side

B
brglm2
ANALYTICS
0.0

A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression

◆ Current state

brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.

◆ Where it's heading

The package's older work assumed the classical regime where observations comfortably outnumber parameters. mdyplFit() and its hd_correction argument target the opposite case, and the follow-up releases are almost entirely about it — Pearson residuals on original responses, aliased parameter handling, the sloe() signal-strength estimator ignoring leverage-one observations. Meanwhile the older surface gets graceful-failure work: brglm_fit() now returns its latest estimates with warnings rather than aborting.

◆ Prediction

Given that 1.0.1 and 1.1.0 are both dominated by mdyplFit follow-ups while the classical path receives only robustness fixes, further work on high-dimensional corrections is the likeliest direction.

Q
qtl2
ANALYTICS
2.5

The standard QTL mapping package in R opened its genome scan to user-supplied likelihood models.

◆ Current state

qtl2 is the R toolkit for QTL mapping in experimental crosses, covering genotype probability calculation, genome scans with and without polygenic effects, permutation testing, SNP association, and the plotting that goes with them. The last year of work has pushed hard in two directions: tooling for high-throughput expression and protein QTL studies, and a generalisation of the scan engine itself so the log-likelihood being maximised can be supplied by the user. Note that the release history reached this feed out of order, so feed position is not a reliable guide to which release came first.

◆ Where it's heading

The eQTL and pQTL direction is the clearest thread — cis-trans plots, hotspot counting over a sliding window, multi-trait scan heat maps, and genome-wide genotype plots all arrived together, which is the toolkit an experiment with thousands of traits needs rather than one with a handful. Running underneath it is a steady generalisation of the core: a scan function that accepts an arbitrary likelihood, permutations that work with alternative scan functions, full variance-covariance output from single-position fits. Performance and parallelism get attention each cycle, including a more considerate default that leaves one core free. The rest is the ordinary maintenance of a long-lived package — renames to avoid tidyverse collisions, compiler warnings, and correctness fixes on specific cross types.

◆ Prediction

With scan1gen and permutation support for alternative scan functions in place, the natural next step is more model types built on that hook rather than more special-cased scan functions; the entries do not indicate which models are planned.

Alternatives to brglm2 and qtl2

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 brglm2 or qtl2.

See all brglm2 alternatives → · See all qtl2 alternatives →

Recent activity from brglm2 and qtl2

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 27d agoqtl2chr_lengths() extended to cross2 objects
  2. 1mo agoqtl2A genome scan that takes your own likelihood function
  3. 2mo agoqtl2Hotspot counting and cis-trans plots for eQTL studies
  4. 3mo agoqtl2Confidence interval plotting, plus a documentation correction
  5. 3mo agobrglm2brglm2 v1.1.0
  6. 8mo agobrglm2brglm2 v1.0.1
  7. 11mo agobrglm21.0.0 adds maximum DY-prior penalized likelihood for logistic regression
  8. 1y agobrglm2brglm2 v0.9.3
  9. 1y agobrglm2brglm2 v0.9.2
  10. 1y agoqtl2Finer-grained parallelism for kinship-based scans
  11. 1y agoqtl2CSV readers renamed to avoid the readr collision
  12. 3y agobrglm2brglm2 v0.9.1

Frequently asked questions

What is the difference between brglm2 and qtl2?

Both compete on the same themes — r-package — within Analytics. qtl2 is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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.

Is brglm2 better than qtl2?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. qtl2 is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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.

What are the best alternatives to brglm2?

Top brglm2 alternatives in Analytics are ranked by recent ship velocity. Browse the "brglm2 alternatives" section above for the current picks, or visit /alternatives/brglm2 for the full list with editorial commentary on each.

What are the best alternatives to qtl2?

Top qtl2 alternatives in Analytics are ranked by recent ship velocity. Browse the "qtl2 alternatives" section above for the current picks, or visit /alternatives/qtl2 for the full list with editorial commentary on each.