mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
parameters vs see
A side-by-side editorial comparison of parameters and see — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:easystats
parameters vs see: at a glance
What is parameters?
easystats' parameters package absorbs one more model class every few weeks
parameters extracts and formats coefficients from an enormous range of R model objects, and its releases read as a running ledger of that range expanding — lavaan and lavaan.mi, survey, lcmm, glmmTMB, fixest, marginaleffects, ordinal. Recent versions ship roughly monthly with a mix of new support, new arguments, and fixes for label handling and standard errors. The most consequential recent change is behavioral: post-hoc standardization no longer standardizes the intercept, setting it and its inferential statistics to NA.
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.
parameters vs see: editorial side-by-side
parameters
0.0ANALYTICS
easystats' parameters package absorbs one more model class every few weeks
◆ Current state
parameters extracts and formats coefficients from an enormous range of R model objects, and its releases read as a running ledger of that range expanding — lavaan and lavaan.mi, survey, lcmm, glmmTMB, fixest, marginaleffects, ordinal. Recent versions ship roughly monthly with a mix of new support, new arguments, and fixes for label handling and standard errors. The most consequential recent change is behavioral: post-hoc standardization no longer standardizes the intercept, setting it and its inferential statistics to NA.
◆ Where it's heading
The package's job is to be the universal adapter for model output, so its roadmap is effectively set by what the R modelling ecosystem produces. Two threads are visible beyond coverage: getting standard errors right for awkward cases such as frailty terms and robust vcov matrices, and getting labels right when factors are converted on the fly or character variables appear in a formula. Interoperability inside easystats keeps tightening, with equivalence_test() gaining methods for modelbased objects.
◆ Prediction
Given the cadence, the next release will most likely add another model class alongside label and standard-error fixes rather than change how the package works.
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 parameters 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 parameters or see.
mlr3viz
mlr3viz keeps the ecosystem's plots working while the plots themselves move out
mlr3tuning
mlr3tuning is rebuilding its async machinery under a stable public surface
timetk
timetk swallowed anomalize whole, then went quiet for two years
modelbased
modelbased is turning marginal effects into a full contrast grammar
tidyposterior
A finished Bayesian model-comparison package in pure maintenance mode
See all parameters alternatives → · See all see alternatives →
Recent activity from parameters and see
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoseesee 0.14.1 adds plots for prior checks and grouped means
- 1mo agoparametersparameters 0.29.2 extends lavaan support and fixes label dropping
- 2mo agoseesee 0.14.0 renders factor loadings as node-edge graphs
- 2mo agoparametersparameters 0.29.1 adds a cluster argument and fixes vcov handling
- 3mo agoparametersparameters 0.29.0 stops standardizing the intercept in post-hoc methods
- 6mo agoseesee 0.13.0 fixes reversed plot sorting, adds theme arguments
- 8mo agoparametersparameters 0.28.3 adds Kenward-Roger and Satterthwaite for glmmTMB
- 11mo agoseesee 0.12.0 extends normality checks to psych factor models
- 11mo agoparametersparameters 0.28.2 updates tests for the latest fixest release
- 11mo agoparametersparameters 0.28.1 adds robust standard errors for glmmTMB
- 1y agoseesee 0.11.0 scales theme elements with base_size
- 1y agoseesee 0.10.0 plots random-effect group levels for mixed models
Frequently asked questions
What is the difference between parameters and see?
Both compete on the same themes — easystats — within Analytics. parameters 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 parameters better than see?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. parameters 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 parameters?
Top parameters alternatives in Analytics are ranked by recent ship velocity. Browse the "parameters alternatives" section above for the current picks, or visit /alternatives/parameters-r 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.