nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Comparison · Analytics
qtl2 vs vim
A side-by-side editorial comparison of qtl2 and vim — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
qtl2 vs vim: at a glance
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.
What is vim?
Six dormant years end with a correctness audit across VIM's entire imputation surface
VIM handles visualization and imputation of missing values in R, with kNN, hot-deck, iterative robust model-based imputation and matching-based methods. Development effectively stopped after 6.0.0 in 2020. Version 7.2.0 arrives in July 2026 as an explicitly framed correctness milestone: MI-properness warnings, ordered-factor preservation, a keep_all_columns option, list returns from irmi(mi>1), repairs to imputeRobust and imputeRobustChain, cellwise IRWLS and initial-weight fixes, and kNN and gowerD mixed-scaling corrections with a weightDist guard.
qtl2 vs vim: editorial side-by-side
qtl2
2.5ANALYTICS
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.
vim
0.0ANALYTICS
Six dormant years end with a correctness audit across VIM's entire imputation surface
◆ Current state
VIM handles visualization and imputation of missing values in R, with kNN, hot-deck, iterative robust model-based imputation and matching-based methods. Development effectively stopped after 6.0.0 in 2020. Version 7.2.0 arrives in July 2026 as an explicitly framed correctness milestone: MI-properness warnings, ordered-factor preservation, a keep_all_columns option, list returns from irmi(mi>1), repairs to imputeRobust and imputeRobustChain, cellwise IRWLS and initial-weight fixes, and kNN and gowerD mixed-scaling corrections with a weightDist guard.
◆ Where it's heading
The release notes describe an audit — Wave 1 plus tail — rather than a feature cycle, and the fixes cluster around statistical validity: whether multiple imputation is proper, whether factor ordering survives, whether distance scaling across mixed variable types is right. Those are the properties users cannot easily verify themselves, so a package correcting them after six years is implicitly restating what its earlier output was worth. The notes also name a forthcoming R Journal paper under the name vimpute, which points at a successor or companion identity.
◆ Prediction
The entries call this a stable reference point for a paper and refer to Wave 1, so a further audit wave is the most likely next release; the vimpute naming is worth watching but the entries do not say what it is.
Alternatives to qtl2 and vim
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 qtl2 or vim.
susier
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
UCell
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
detectseparation
A diagnostic package that generalized past its own name, then learned to say which kind of separation it found
brglm2
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
bayestools
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
Recent activity from qtl2 and vim
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 27d agoqtl2chr_lengths() extended to cross2 objects
- 1mo agovimCorrectness audit fixes MI-properness, factor order and distance scaling
- 1mo agoqtl2A genome scan that takes your own likelihood function ⚡
- 2mo agoqtl2Hotspot counting and cis-trans plots for eQTL studies
- 3mo agoqtl2Confidence interval plotting, plus a documentation correction
- 1y agoqtl2Finer-grained parallelism for kinship-based scans
- 1y agoqtl2CSV readers renamed to avoid the readr collision
- 6y agovimAdds ranger-based imputation, drops survey and GUI support
- 6y agovimAdds nine example datasets and splits help pages
- 6y agovimAdds matchImpute() and random-forest augmented kNN
- 6y agovimOrdered factor support and ordinal regression in irmi()
- 6y agovimBug fixes for kNN, hotdeck and irmi input handling
Frequently asked questions
What is the difference between qtl2 and vim?
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 qtl2 better than vim?
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 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.
What are the best alternatives to vim?
Top vim alternatives in Analytics are ranked by recent ship velocity. Browse the "vim alternatives" section above for the current picks, or visit /alternatives/vim for the full list with editorial commentary on each.