rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
A side-by-side editorial comparison of mlr3mbo and mlr3proba — release velocity, themes, recent moves, and the top alternatives to consider.
mlr3mbo picked its defaults from a benchmark study, not from taste
mlr3mbo does model-based and Bayesian optimisation for mlr3. Its 1.0.0 release added a dictionary of pre-built acquisition-function optimisers and, more consequentially, replaced the default surrogate, acquisition function and optimiser settings with values derived from a large-scale benchmark study. The releases since are corrections to the acquisition-optimiser path exposed by that new default configuration.
mlr3proba is shedding weight as its survival work moves into sibling packages
mlr3proba provides probabilistic supervised learning for mlr3 — survival analysis, density estimation, and the measures that go with them. Recent releases are almost entirely upkeep: a distr6 fork to work around an upstream problem, an ooplah fix, a predict-type correction, and registration in mlr_reflections$loaded_packages. The one deletion is telling, with LearnerDensPenalized removed after pendensity left CRAN.
mlr3mbo does model-based and Bayesian optimisation for mlr3. Its 1.0.0 release added a dictionary of pre-built acquisition-function optimisers and, more consequentially, replaced the default surrogate, acquisition function and optimiser settings with values derived from a large-scale benchmark study. The releases since are corrections to the acquisition-optimiser path exposed by that new default configuration.
The package has moved from a toolkit that expected users to assemble a Bayesian optimisation loop into one with a defensible default loop, and the recent fixes — warm-start sizing on multi-objective archives, silently discarded terminators, stale x_domain values — are the consequences of more people running the default path.
Expect continued hardening of the acquisition-optimiser classes rather than new acquisition functions.
mlr3proba provides probabilistic supervised learning for mlr3 — survival analysis, density estimation, and the measures that go with them. Recent releases are almost entirely upkeep: a distr6 fork to work around an upstream problem, an ooplah fix, a predict-type correction, and registration in mlr_reflections$loaded_packages. The one deletion is telling, with LearnerDensPenalized removed after pendensity left CRAN.
The package is being pared back rather than extended. Its README now points at survdistr and mlr3cmprsk as matured alternatives for parts of what it covers, which reads as scope being handed off, while the Cox proportional-hazards autoplot arrived from mlr3viz in the other direction. Several fixes exist to route around dependencies that broke or disappeared, which is the recurring cost of building on a long chain of specialized CRAN packages.
The dependency churn suggests more consolidation — further reliance on survdistr and mlr3cmprsk, and more learners retired when the package underneath them goes unmaintained.
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 mlr3mbo or mlr3proba.
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
writexl spent nine years refusing to do formatting, then shipped all of it in 2.0.0.
rgbif is steadily pushing users off paged searching and onto real downloads.
rstanarm is community-maintained now, tracking Stan and lme4 rather than adding models.
taxa started a ground-up rewrite in 2021 and has published almost nothing since.
rotl's whole release history is keeping name matching honest against a moving taxonomy.
See all mlr3mbo alternatives → · See all mlr3proba alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — mlr3 — within Analytics. mlr3mbo 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3mbo 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.
Top mlr3mbo alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3mbo alternatives" section above for the current picks, or visit /alternatives/mlr3mbo for the full list with editorial commentary on each.
Top mlr3proba alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3proba alternatives" section above for the current picks, or visit /alternatives/mlr3proba for the full list with editorial commentary on each.