ANALYTICS
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
Alternatives · analytics tools
forestly alternatives
The best forestly alternatives in analytics tools, ranked by Sparkpulse's velocity_score.
Updated Aug 14, 2026
Looking for the best alternatives to forestly? Sparkpulse tracks and ranks 12 alternatives in analytics tools by shipping velocity — how frequently each ships meaningful updates, verified from official changelogs. For reference, forestly shipped 0 meaningful updates in the last 30 days and carries a velocity score of 0.0 out of 10 in 2026. The alternatives below are ranked the same way, so you're comparing real release momentum, not marketing claims.
About forestly
forestly built an interactive safety review tool, then taught it to produce submission-ready RTF.
forestly renders adverse-event forest plots as interactive reactable widgets — filterable by AE category, with sliders for incidence thresholds and a toggle for the risk-difference column. Version 0.1.3 added `rtf_static_forestly()` for static RTF output, and 0.1.4 has been about giving the display owner control over what reviewers see: the CSV download button, the AE filter label, and the diff toggle can each be switched off.
Velocity 0.0 · Last update 1h ago
Top 12 alternatives to forestly
Ranked by recent ship velocity. Tap any card for the full editorial breakdown, or pivot to a head-to-head.
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Six years since the last functional change, and Google renamed the service it wraps in the release before that
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The meta-package ships almost nothing, which is exactly what a version-pinning shim should do
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The DataONE bundler learned to edit packages in 2017 and has coasted on that ever since
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Seven years dormant, then two releases dragging every census boundary from 2020 to 2024
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Feature-complete since 2021, and every release since has been paying CRAN's C API bill
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A fixed-design trial simulator grew a pluggable test framework, then spent a year proving the numbers
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One document API over six databases, and every release is spent absorbing their JSON engines' churn
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Sparse vectors stopped being a storage trick and became something you can do arithmetic on
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A Darwin Core validator that went quiet for two years, then surfaced only to raise its R floor
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Four releases in three years, each one teaching the serializer about a model type it couldn't carry
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The R port of LIME has shipped one commit in three years, and it was an xgboost compatibility patch
forestly vs alternatives — shipping velocity at a glance
Velocity score (0–10) and meaningful releases shipped in the last 30 days, from official changelogs. Higher = shipping faster.
| Product | Velocity | Sparks · 30d | Focus areas | Latest release |
|---|---|---|---|---|
| forestly (baseline) | 0.0 | 0 | clinical-safetyadverse-eventsdata-visualization | Static RTF forest plots join the interactive output |
| chattr | 0.0 | 0 | llmrstudioide-integration | All model integration moves to ellmer, direct backends removed |
| cloudml | 0.0 | 0 | machine-learninggoogle-cloudtensorflow | — |
| tidymodels | 0.0 | 0 | tidymodelsmeta-packagedependency-management | — |
| datapack | 0.0 | 0 | research-datadataoneprovenance | Assembled data packages become editable in place |
| USAboundaries | 0.0 | 0 | geospatialcensus-datasf | Data split into a companion package; all boundaries become sf |
| slider | 0.0 | 0 | sliding-windowstidyversec-api-compliance | — |
| simtrial | 0.0 | 0 | clinical-trialsgroup-sequentialsurvival-analysis | RMST and milestone tests, plus a user-definable cut and test framework |
| nodbi | 0.0 | 0 | document-databasesjsonduckdb | Query results get consistent column types; fast NDJSON import reaches SQLite and Postgres |
| sparsevctrs | 0.0 | 0 | sparse-datatidymodelsaltrep | Scalar and element-wise arithmetic for sparse vectors |
| dwctaxon | 0.0 | 0 | darwin-coretaxonomydata-validation | — |
| bundle | 0.0 | 0 | serializationtidymodelsmodel-deployment | — |
| lime | 0.0 | 0 | explainabilitymachine-learningmaintenance-mode | — |
The 12 best forestly alternatives, in depth
1. chattr · velocity 0.0
Chattr deleted every LLM integration it had written and outsourced the lot to ellmer.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “All model integration moves to ellmer, direct backends removed”.
Where forestly leans on clinical safety, adverse events and data visualization, chattr focuses on llm, rstudio and ide integration.
chattr and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
2. cloudml · velocity 0.0
Six years since the last functional change, and Google renamed the service it wraps in the release before that.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where forestly leans on clinical safety, adverse events and data visualization, cloudml focuses on machine learning, google cloud and tensorflow.
cloudml and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
3. tidymodels · velocity 0.0
The meta-package ships almost nothing, which is exactly what a version-pinning shim should do.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where forestly leans on clinical safety, adverse events and data visualization, tidymodels focuses on tidymodels, meta package and dependency management.
tidymodels and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full tidymodels trajectory → · Compare forestly vs tidymodels →
4. datapack · velocity 0.0
The DataONE bundler learned to edit packages in 2017 and has coasted on that ever since.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Assembled data packages become editable in place”.
Where forestly leans on clinical safety, adverse events and data visualization, datapack focuses on research data, dataone and provenance.
datapack and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
5. USAboundaries · velocity 0.0
Seven years dormant, then two releases dragging every census boundary from 2020 to 2024.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Data split into a companion package; all boundaries become sf”.
Where forestly leans on clinical safety, adverse events and data visualization, USAboundaries focuses on geospatial, census data and sf.
USAboundaries and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full USAboundaries trajectory → · Compare forestly vs USAboundaries →
6. slider · velocity 0.0
Feature-complete since 2021, and every release since has been paying CRAN's C API bill.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where forestly leans on clinical safety, adverse events and data visualization, slider focuses on sliding windows, tidyverse and c api compliance.
slider and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
7. simtrial · velocity 0.0
A fixed-design trial simulator grew a pluggable test framework, then spent a year proving the numbers.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “RMST and milestone tests, plus a user-definable cut and test framework”.
Where forestly leans on clinical safety, adverse events and data visualization, simtrial focuses on clinical trials, group sequential and survival analysis.
simtrial and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
8. nodbi · velocity 0.0
One document API over six databases, and every release is spent absorbing their JSON engines' churn.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Query results get consistent column types; fast NDJSON import reaches SQLite and Postgres”.
Where forestly leans on clinical safety, adverse events and data visualization, nodbi focuses on document databases, json and duckdb.
nodbi and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
9. sparsevctrs · velocity 0.0
Sparse vectors stopped being a storage trick and became something you can do arithmetic on.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Scalar and element-wise arithmetic for sparse vectors”.
Where forestly leans on clinical safety, adverse events and data visualization, sparsevctrs focuses on sparse data, tidymodels and altrep.
sparsevctrs and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full sparsevctrs trajectory → · Compare forestly vs sparsevctrs →
10. dwctaxon · velocity 0.0
A Darwin Core validator that went quiet for two years, then surfaced only to raise its R floor.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where forestly leans on clinical safety, adverse events and data visualization, dwctaxon focuses on darwin core, taxonomy and data validation.
dwctaxon and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
11. bundle · velocity 0.0
Four releases in three years, each one teaching the serializer about a model type it couldn't carry.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where forestly leans on clinical safety, adverse events and data visualization, bundle focuses on serialization, tidymodels and model deployment.
bundle and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
12. lime · velocity 0.0
The R port of LIME has shipped one commit in three years, and it was an xgboost compatibility patch.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where forestly leans on clinical safety, adverse events and data visualization, lime focuses on explainability, machine learning and maintenance mode.
lime and forestly have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Frequently asked questions
What are the best alternatives to forestly?
The top forestly alternatives we currently track in analytics tools are chattr, cloudml, tidymodels, datapack, USAboundaries, ranked by recent ship velocity.
How is this list of forestly alternatives ranked?
Alternatives are ranked by Sparkpulse's velocity_score — release cadence + 30-day spark count + sector-relative ship rate.
Can I compare forestly directly with one of these alternatives?
Yes — every card has a "Compare with forestly" link to a side-by-side /compare page.