PurpleAir
The R client for PurpleAir sensors keeps finding its time-averaging was wrong.
Comparison · Analytics
glmbayes vs hydroloom
A side-by-side editorial comparison of glmbayes and hydroloom — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
glmbayes vs hydroloom: at a glance
What is glmbayes?
A GPU-accelerated Bayesian GLM package buys its way into the standard R Bayesian toolchain
glmbayes fits Bayesian generalized linear models with optional OpenCL acceleration. The last four months moved it from a package with its own vocabulary to one that answers the insight and bayestestR generics the rest of the R Bayesian ecosystem is built on, while pushing the OpenCL kernels out into a separate nmathopencl dependency that carries CRAN Windows binaries. It returned to CRAN in August after an archival over a configure policy issue.
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.
glmbayes vs hydroloom: editorial side-by-side
glmbayes
6.3ANALYTICS
A GPU-accelerated Bayesian GLM package buys its way into the standard R Bayesian toolchain
◆ Current state
glmbayes fits Bayesian generalized linear models with optional OpenCL acceleration. The last four months moved it from a package with its own vocabulary to one that answers the insight and bayestestR generics the rest of the R Bayesian ecosystem is built on, while pushing the OpenCL kernels out into a separate nmathopencl dependency that carries CRAN Windows binaries. It returned to CRAN in August after an archival over a configure policy issue.
◆ Where it's heading
The arc is about removing reasons not to use it. GPU support was previously blocked on Windows because the OpenCL kernels were vendored; splitting them into a CRAN package with binaries fixed that. The ecosystem work does the same thing for tooling — a glmb fit now responds to get_parameters, get_priors, simulate_prior and check_prior, so it drops into workflows built around easystats rather than requiring its own. The CRAN archival and the configure fixes that followed show how much of the effort goes into distribution rather than modelling.
◆ Prediction
get_priors() returning the full prior specification rather than a marginal table is the kind of detail that invites further bayestestR integration, and the diagnostic surface is the least built-out part of what has shipped so far.
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 glmbayes 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 glmbayes or hydroloom.
piecepackr
A board game graphics package runs one of the most disciplined deprecation cycles in R.
e2tree
The explainable-ensemble-tree package now measures whether its own explanations are faithful.
discretefdr
The discrete-data FDR package is being pared into one piece of a larger multiple-testing suite.
contentanalysis
A scientific-text analysis package moved from counting citations to classifying argument structure.
relialearnr
The teaching arm of an R reliability suite keeps pace with whatever its analysis siblings ship.
See all glmbayes alternatives → · See all hydroloom alternatives →
Recent activity from glmbayes and hydroloom
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 10d agoglmbayesBack on CRAN after a configure policy fix
- 22d agoglmbayesOpenCL split out to nmathopencl; insight and bayestestR integration ⚡
- 1mo agoglmbayesMulti-response models and conjugate GLM priors
- 2mo agohydroloomhydroloom v1.2.0 ⚡
- 3mo agoglmbayesOpenCL kernels restructured and a binomial GPU bug fixed
- 3mo agoglmbayesVersion bump for CRAN resubmission
- 5mo agohydroloomTest tolerances relaxed for CRAN Fedora checks
- 5mo agohydroloomNetwork subsetting and divergence-routed accumulation
- 10mo agohydroloomSort and indexing fixes
- 1y agoglmbayesCRAN-ready beta with the core S3 interface
- 1y agohydroloomUpmain and downmain navigation for non-dendritic networks
- 2y agohydroloomInitial release completing the nhdplusTools migration
Frequently asked questions
What is the difference between glmbayes and hydroloom?
Both compete on the same themes — r-package — within Analytics. glmbayes is currently shipping more aggressively (velocity 6.3 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.
Is glmbayes better than hydroloom?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. glmbayes is currently shipping more aggressively (velocity 6.3 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.
What are the best alternatives to glmbayes?
Top glmbayes alternatives in Analytics are ranked by recent ship velocity. Browse the "glmbayes alternatives" section above for the current picks, or visit /alternatives/glmbayes 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.