Tidymodels in Analytics — SaaS tools & trends 2026
38 products Sparkpulse tracks are shipping around tidymodels in Analytics. The highest-velocity tidymodels products right now are pysparklyr, factoextra and themis (ranked by Sparkpulse's velocity score). The theme runs across 3 sectors, so the momentum you see here is drawn from products competing in different corners of the market. Their velocity scores span 0.0 to 3.8 out of 10, a spread that shows how unevenly shipping cadence is distributed across the field. Below: every tracked product carrying this theme, updated from release data. Everything on this page is regenerated from verified release data, so the ranking reflects the latest changelogs rather than a static list.
#01pysparklyrPosit's Spark Connect bridge keeps adding backends — and now runs tidymodels tuning on the cluster.3.8alternatives →#02factoextrafactoextra woke from six years of silence and stopped being a FactoMineR front-end3.8alternatives →#03themisthemis is back to adding real resampling algorithms after a documentation-heavy stretch.2.5alternatives →#04tabnetA tabular deep-learning model in R that keeps widening what counts as a tabular task.2.5alternatives →#05modeltimemodeltime built conformal intervals in, then went quiet on features.0.0alternatives →#06tidymodelsThe meta-package ships almost nothing, which is exactly what a version-pinning shim should do0.0alternatives →#07waywiserSpatial model assessment that spent the last year on cross-platform arithmetic and CRAN rules.0.0alternatives →#08dialsdials is quietly registering the tuning parameters for tidymodels' deep-learning push0.0alternatives →#09bundleFour releases in three years, each one teaching the serializer about a model type it couldn't carry0.0alternatives →#10workflowsetsworkflowsets keeps widening what counts as a model worth comparing.0.0alternatives →#11hardhathardhat keeps adding the contracts tidymodels needs next0.0alternatives →#12censoredcensored keeps survival models aligned with parsnip's shifting prediction contracts0.0alternatives →#13bonsaibonsai keeps widening tidymodels' boosted-tree engine bench, catboost most recently0.0alternatives →#14modeltime.ensemblemodeltime.ensemble wakes after four years, and the work is all tune 2.0 compatibility.0.0alternatives →#15modeldataThe tidymodels example-data package grows one dataset at a time, on nobody's schedule0.0alternatives →#16workflowsThe tidymodels pipeline grew a third stage, and it happens after the model runs.0.0alternatives →#17yardstickyardstick made fairness metrics a first-class part of tidymodels evaluation0.0alternatives →#18textrecipesText features finally stay sparse all the way to the model.0.0alternatives →#19cubistThe R port of Quinlan's Cubist gets reproducibility fixes, not new modelling0.0alternatives →#20tidyposteriorA finished Bayesian model-comparison package in pure maintenance mode0.0alternatives →#21finetunefinetune tracks tune's evolving contracts more than it advances racing itself0.0alternatives →#22fastmlfastml added survival modelling and leakage-proof resampling, moving past classification and regression.0.0alternatives →#23poissonregpoissonreg gave its models away to parsnip and kept the glue — now it just keeps glmnet honest.0.0alternatives →#24discrimdiscrim settled into a thin engine shim after handing its model definitions to parsnip.0.0alternatives →#25modeltime.resamplemodeltime.resample exists to keep backtesting working as tidymodels shifts underneath it.0.0alternatives →#26embedembed keeps adding encoding steps while shedding its deep-learning dependencies0.0alternatives →#27tunetune extends tuning past the model itself to postprocessors, and adds a second parallel backend0.0alternatives →#28tidyclusttidyclust just tripled the model types it can fit, and handed finalization back to tune0.0alternatives →#29BORGA cross-validation guard that refuses to run random CV on dependent data unless you insist0.0alternatives →#30healthyR.aiA healthyverse machine-learning helper in maintenance: one new function in three years.0.0alternatives →#31stacksModel stacking in tidymodels, quietly migrating off foreach and onto future0.0alternatives →#32butcherbutcher expands from trimming models to trimming whole tidymodels workflows0.0alternatives →#33bruleetidymodels' torch backend grew from MLPs into a tabular deep learning suite with foundation models.0.0alternatives →#34vetiverPosit's MLOps package went quiet for two years, then came back to keep up with recipes.0.0alternatives →#35probablyThe package that made calibration a step instead of an afterthought.0.0alternatives →#36orbitalTurning fitted tidymodels into SQL, one model family at a time — and the boosting engines just landed.0.0alternatives →#37desirability2desirability2 is making multi-metric model selection a first-class tidymodels step.0.0alternatives →#38sparsevctrsSparse vectors stopped being a storage trick and became something you can do arithmetic on0.0alternatives →
Frequently asked questions about tidymodels
Which SaaS tools ship tidymodels in Analytics in 2026?
pysparklyr, factoextra, themis, tabnet, modeltime, and 33 more — the tidymodels products Sparkpulse tracks in Analytics, ranked by shipping velocity from verified changelogs.
Which tidymodels product has the highest shipping velocity?
pysparklyr, with the top velocity score (3.8/10) in Analytics — Sparkpulse's velocity score is derived from verified release data.
How many products are shipping around tidymodels?
Sparkpulse currently tracks 38 products carrying the tidymodels theme in Analytics, updated continuously from verified release data.