pins
pins keeps adding a storage backend per release while retiring its original API
Comparison · Analytics
insight vs see
A side-by-side editorial comparison of insight and see — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:easystats
insight vs see: at a glance
What is insight?
insight quietly widens the set of model objects the easystats ecosystem can read
insight is the extraction layer under the easystats packages — it answers what a fitted model's parameters, data, variance and priors are, for whatever object it is handed. The 1.4.x and 1.5.x releases read as a steady widening of that support list: tidymodels workflows, cmdstanr fits, rstpm2 survival models, lavaan variance-covariance, mice imputations.
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.
insight vs see: editorial side-by-side
insight
0.0ANALYTICS
insight quietly widens the set of model objects the easystats ecosystem can read
◆ Current state
insight is the extraction layer under the easystats packages — it answers what a fitted model's parameters, data, variance and priors are, for whatever object it is handed. The 1.4.x and 1.5.x releases read as a steady widening of that support list: tidymodels workflows, cmdstanr fits, rstpm2 survival models, lavaan variance-covariance, mice imputations.
◆ Where it's heading
Two things move together here. The support list grows toward objects produced outside the easystats world, and performance work targets the helpers that everything else calls — compact_list(), is_empty_object(), find_parameters() on mgcv models. New functions appear occasionally (get_simulated(), vcovFPC()) but the center of gravity is coverage, not capability.
◆ Prediction
Expect further model classes to be added as downstream easystats packages need them, and continued alignment with R-devel behavior changes like the weighted-residuals revision.
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 insight 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 insight or see.
tsibble
tsibble shipped one release in five and a half years - the data structure is finished
yardstick
yardstick made fairness metrics a first-class part of tidymodels evaluation
tune
tune extends tuning past the model itself to postprocessors, and adds a second parallel backend
leaflet
leaflet relicensed to MIT and finished migrating off R's retired spatial stack
ggpubr
ggpubr reached 1.0.0 with p-value formatting presets for specific journals
Recent activity from insight 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 agoinsightcompact_list() performance and lavaan variance-covariance support
- 2mo agoseesee 0.14.0 renders factor loadings as node-edge graphs
- 2mo agoinsightcmdstanr support and finite-population-corrected variance
- 4mo agoinsightget_simulated() added; rstpm2 survival models supported
- 6mo agoinsightWeighted residuals revised to match R 4.6.0
- 6mo agoseesee 0.13.0 fixes reversed plot sorting, adds theme arguments
- 6mo agoinsighttidymodels workflow objects become readable
- 8mo agoinsightlme4 convergence and fixest data extraction fixes
- 11mo agoseesee 0.12.0 extends normality checks to psych factor models
- 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 insight and see?
Both compete on the same themes — easystats — within Analytics. insight 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 insight better than see?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. insight 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 insight?
Top insight alternatives in Analytics are ranked by recent ship velocity. Browse the "insight alternatives" section above for the current picks, or visit /alternatives/insight 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.