nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Comparison · Analytics
brglm2 vs hydroloom
A side-by-side editorial comparison of brglm2 and hydroloom — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
brglm2 vs hydroloom: at a glance
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.
What is hydroloom?
USGS puts a type system over its river network toolkit so errors surface at dispatch
hydroloom builds and navigates hydrologic flow networks, carrying functionality migrated out of nhdplusTools. Version 1.2.0 introduces an S3 class hierarchy — hy_topo, hy_leveled, hy_node, hy_flownetwork — assigned automatically by hy() and by producer functions, letting the package validate input at dispatch time and emit guided errors. Outlet detection is now defined explicitly: a row is an outlet when its toid is not in id, with reserved values, NA and implicit absence all accepted.
brglm2 vs hydroloom: editorial side-by-side
brglm2
0.0ANALYTICS
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.
hydroloom
0.0ANALYTICS
USGS puts a type system over its river network toolkit so errors surface at dispatch
◆ Current state
hydroloom builds and navigates hydrologic flow networks, carrying functionality migrated out of nhdplusTools. Version 1.2.0 introduces an S3 class hierarchy — hy_topo, hy_leveled, hy_node, hy_flownetwork — assigned automatically by hy() and by producer functions, letting the package validate input at dispatch time and emit guided errors. Outlet detection is now defined explicitly: a row is an outlet when its toid is not in id, with reserved values, NA and implicit absence all accepted.
◆ Where it's heading
The package spent its first releases porting and broadening — non-dendritic network support, divergence routing, subsetting that follows diversions out of a basin — and has now turned to making that surface safe to use. The class hierarchy is the structural expression of that turn: instead of every function re-checking whether a data frame has the columns it needs, the type carries the guarantee. The explicit outlet rule resolves a category of failure where valid networks errored on NA or orphan toid values.
◆ Prediction
The release notes flag that subclass attributes are stripped by standard dplyr operations, which is the kind of rough edge that usually generates follow-up work — expect attribute preservation or restoration helpers next.
Alternatives to brglm2 and hydroloom
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 hydroloom.
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
bayestools
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
robma
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
See all brglm2 alternatives → · See all hydroloom alternatives →
Recent activity from brglm2 and hydroloom
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agohydroloomhydroloom v1.2.0 ⚡
- 3mo agobrglm2brglm2 v1.1.0
- 5mo agohydroloomTest tolerances relaxed for CRAN Fedora checks
- 5mo agohydroloomNetwork subsetting and divergence-routed accumulation
- 8mo agobrglm2brglm2 v1.0.1
- 10mo agohydroloomSort and indexing fixes
- 11mo agobrglm21.0.0 adds maximum DY-prior penalized likelihood for logistic regression ⚡
- 1y agobrglm2brglm2 v0.9.3
- 1y agobrglm2brglm2 v0.9.2
- 1y agohydroloomUpmain and downmain navigation for non-dendritic networks
- 2y agohydroloomInitial release completing the nhdplusTools migration
- 3y agobrglm2brglm2 v0.9.1
Frequently asked questions
What is the difference between brglm2 and hydroloom?
Both compete on the same themes — r-package — within Analytics. brglm2 and hydroloom are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is brglm2 better than hydroloom?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. brglm2 and hydroloom are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). 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 hydroloom?
Top hydroloom alternatives in Analytics are ranked by recent ship velocity. Browse the "hydroloom alternatives" section above for the current picks, or visit /alternatives/hydroloom for the full list with editorial commentary on each.