tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
mlr3cluster vs see
A side-by-side editorial comparison of mlr3cluster and see — release velocity, themes, recent moves, and the top alternatives to consider.
mlr3cluster vs see: at a glance
What is mlr3cluster?
mlr3cluster went from a handful of clusterers to covering the field
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
What is see?
see grows wherever easystats adds a diagnostic, one plot method at a time.
see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.
mlr3cluster vs see: editorial side-by-side
mlr3cluster
0.0ANALYTICS
mlr3cluster went from a handful of clusterers to covering the field
◆ Current state
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
◆ Where it's heading
The package is at the tail end of a coverage push, and the emphasis has shifted from adding algorithms to making the ones it has behave correctly at prediction time — cutting trees at the current k, reclustering coresets, failing informatively on unsupported metric combinations. That is the normal sequence after a rapid expansion.
◆ Prediction
Expect further predict-path corrections and parameter-set alignment across the newly added learners before any more algorithms arrive.
see
0.0ANALYTICS
see grows wherever easystats adds a diagnostic, one plot method at a time.
◆ Current state
see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.
◆ Where it's heading
Growth here is downstream-driven rather than self-directed: see expands to cover new diagnostics as easystats produces them. Running alongside that is a sustained investment in presentation control — theme arguments on plot methods, elements that scale with base_size — which suits users embedding these plots in documents rather than glancing at them interactively.
◆ Prediction
Expect new plot methods to keep arriving in step with performance and parameters releases, with continued theming work rather than any change in the package's scope.
Alternatives to mlr3cluster and see
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 mlr3cluster or see.
textrecipes
Text features finally stay sparse all the way to the model.
probably
The package that made calibration a step instead of an afterthought.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
See all mlr3cluster alternatives → · See all see alternatives →
Recent activity from mlr3cluster and see
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomlr3clusterHierarchical learners now honour k at prediction time
- 1mo agoseesee 0.14.1 adds plots for prior checks and grouped means
- 2mo agomlr3clusterNine new clustering learners in one release ⚡
- 2mo agoseesee 0.14.0 renders factor loadings as node-edge graphs
- 5mo agomlr3clusterCLARA, k-prototypes and spectral clustering learners added
- 6mo agomlr3clusterTyped error classes and probabilistic EM assignments
- 6mo agoseesee 0.13.0 fixes reversed plot sorting, adds theme arguments
- 8mo agomlr3clusterHDBSCAN gains cluster_selection_epsilon
- 11mo agoseesee 0.12.0 extends normality checks to psych factor models
- 1y agoseesee 0.11.0 scales theme elements with base_size
- 1y agomlr3clusterMclust learner brought in line with paradox conventions
- 1y agoseesee 0.10.0 plots random-effect group levels for mixed models
Frequently asked questions
What is the difference between mlr3cluster and see?
They serve adjacent needs but don't currently overlap on shipped themes. mlr3cluster and see 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 mlr3cluster better than see?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3cluster and see 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 mlr3cluster?
Top mlr3cluster alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3cluster alternatives" section above for the current picks, or visit /alternatives/mlr3cluster for the full list with editorial commentary on each.
What are the best alternatives to see?
Top see alternatives in Analytics are ranked by recent ship velocity. Browse the "see alternatives" section above for the current picks, or visit /alternatives/see-r for the full list with editorial commentary on each.