cIRT
A choice-based IRT model published once in 2019 and kept compiling ever since
Comparison · Analytics
MultiSpline vs tulpaRatio
A side-by-side editorial comparison of MultiSpline and tulpaRatio — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
MultiSpline vs tulpaRatio: at a glance
What is MultiSpline?
MultiSpline went from five functions to a full multilevel spline framework in seven weeks.
MultiSpline fits spline-based nonlinear models to multilevel and longitudinal data in R. The package reached CRAN in February 2026 with five functions covering fitting, summary, prediction, plotting and intraclass correlations. Version 0.2.0, seven weeks later, adds cross-classified and nested random-effect structures, automatic knot selection, a multilevel R-squared variance partition, derivative-based interpretation with turning points, model comparison against polynomials, and cluster heterogeneity analysis, while keeping every 0.1.0 call valid.
What is tulpaRatio?
A Bayesian ratio-modelling package that threw out its Stan dependency and wrote its own sampler
ratiod models ratios, rates and proportions hierarchically, with the stated position that a ratio is a derived quantity and inference should run on the latent numerator and denominator processes rather than their quotient. The 1.0.0 release shipped a native HMC/NUTS backend, removing the Stan dependency that packages in this space normally take as given. Everything since has been sampler optimisation, benchmarked against the Stan implementations it replaced.
MultiSpline vs tulpaRatio: editorial side-by-side
MultiSpline
0.0ANALYTICS
MultiSpline went from five functions to a full multilevel spline framework in seven weeks.
◆ Current state
MultiSpline fits spline-based nonlinear models to multilevel and longitudinal data in R. The package reached CRAN in February 2026 with five functions covering fitting, summary, prediction, plotting and intraclass correlations. Version 0.2.0, seven weeks later, adds cross-classified and nested random-effect structures, automatic knot selection, a multilevel R-squared variance partition, derivative-based interpretation with turning points, model comparison against polynomials, and cluster heterogeneity analysis, while keeping every 0.1.0 call valid.
◆ Where it's heading
The arc is a research package being built out into a workflow at speed: 0.1.0 could fit a curve, 0.2.0 can tell you where the curve turns, how much variance each level explains, and whether a spline beats a polynomial at all. Backward compatibility was preserved across that expansion, which suggests the author is building for outside users rather than a single paper. The JOSS submission referenced in 0.1.1 points at academic distribution as the intended channel.
◆ Prediction
With the interpretation and diagnostics layers now in place, the next release will most likely extend the supported model families beyond the current lmer and glmer backends.
tulpaRatio
0.0ANALYTICS
A Bayesian ratio-modelling package that threw out its Stan dependency and wrote its own sampler
◆ Current state
ratiod models ratios, rates and proportions hierarchically, with the stated position that a ratio is a derived quantity and inference should run on the latent numerator and denominator processes rather than their quotient. The 1.0.0 release shipped a native HMC/NUTS backend, removing the Stan dependency that packages in this space normally take as given. Everything since has been sampler optimisation, benchmarked against the Stan implementations it replaced.
◆ Where it's heading
The feed reads as one architectural bet followed by the work to justify it. After the native backend landed, the releases are a steady march of gradient and adaptation work — hand-coded gradients for more model families, L-BFGS mass matrix adaptation, an O2 build — each measured as a speed multiple against Stan. Coverage is tracked openly as a fraction (48 of 60 hand-coded configs), and unresolved problems are named rather than buried, including a deferred GP spatial bug.
◆ Prediction
The hand-coded gradient coverage count is the visible backlog, so the next releases most likely close the remaining configs and resolve the GP spatial issue that the benchmark release explicitly deferred.
Alternatives to MultiSpline and tulpaRatio
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 MultiSpline or tulpaRatio.
MetaboAnalystR
The R engine behind MetaboAnalyst closes the gap from raw spectra to biological interpretation
fmtr
Rebuilding SAS's formatting layer in R, one format specification at a time
betaselectr
Standardised coefficients for models where standardising everything is wrong — but the feed only links out
SSN2
Stream-network spatial models learning to run on data that no longer fits in memory
BiocManager
Bioconductor's installer, frozen at 1.30.x and tuned almost entirely through environment variables
See all MultiSpline alternatives → · See all tulpaRatio alternatives →
Recent activity from MultiSpline and tulpaRatio
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 4mo agoMultiSplineCross-classified and nested structures turn MultiSpline into a framework ⚡
- 5mo agoMultiSplineMultiSpline v0.1.1
- 5mo agoMultiSplineMultiSpline v0.1.0 - Initial Release
- 5mo agoMultiSplinev0.1.0.1
- 6mo agotulpaRatioHand-coded gradients reach binomial zero-inflated and hurdle models
- 6mo agotulpaRatioGaussian process sampling reaches roughly 4x Stan
- 7mo agotulpaRatioL-BFGS mass matrix adaptation for MSGP models
- 7mo agotulpaRatioBenchmarks published for 35 of 40 model configurations
- 7mo agotulpaRatioFirst stable release ships a native HMC/NUTS backend, no Stan required ⚡
Frequently asked questions
What is the difference between MultiSpline and tulpaRatio?
Both compete on the same themes — r-package — within Analytics. MultiSpline and tulpaRatio 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 MultiSpline better than tulpaRatio?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. MultiSpline and tulpaRatio 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 MultiSpline?
Top MultiSpline alternatives in Analytics are ranked by recent ship velocity. Browse the "MultiSpline alternatives" section above for the current picks, or visit /alternatives/multispline for the full list with editorial commentary on each.
What are the best alternatives to tulpaRatio?
Top tulpaRatio alternatives in Analytics are ranked by recent ship velocity. Browse the "tulpaRatio alternatives" section above for the current picks, or visit /alternatives/tulparatio for the full list with editorial commentary on each.