← Back to home
Comparison · Analytics

collinear vs minimaxapprox

A side-by-side editorial comparison of collinear and minimaxapprox — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r package

collinear vs minimaxapprox: at a glance

Featurecollinearminimaxapprox
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesmulticollinearity, variable selection, vif, breaking changesnumerical analysis, approximation theory, remez algorithm, correctness
Last editorial update45m ago50m ago
WebsiteVisit →Visit →

What is collinear?

collinear has broken its API twice to stop making the user pick thresholds.

collinear removes multicollinearity from predictor sets through pairwise correlation and VIF filtering, with a preference order deciding which variable survives each conflict. Two major versions in thirteen months each rewrote the interface: 2.0.0 extended every function to any combination of categorical and numeric responses and predictors, and 3.0.0 moved to multiple responses, restructured the output into classed objects, and made both filtering thresholds adaptive by default. Version 3.0.1 is the first release since that is purely repair.

Read the full collinear trajectory →

What is minimaxapprox?

minimaxapprox found its Remez exchange had been silently returning the wrong minimax.

minimaxapprox computes minimax polynomial and rational approximations to functions via the Remez exchange algorithm. Version 0.6.0 is a correctness release of unusual depth: the exchange was redesigned after two structural defects were found that let it converge to a reference-local fixed point that is not the global minimax, with no warning. The worked example in the notes has atan on [0,3] at degree 3 reporting an expected error 1.8 times smaller than the returned approximation's true maximum error.

Read the full minimaxapprox trajectory →

collinear vs minimaxapprox: editorial side-by-side

C
collinear
ANALYTICS
0.0

collinear has broken its API twice to stop making the user pick thresholds.

◆ Current state

collinear removes multicollinearity from predictor sets through pairwise correlation and VIF filtering, with a preference order deciding which variable survives each conflict. Two major versions in thirteen months each rewrote the interface: 2.0.0 extended every function to any combination of categorical and numeric responses and predictors, and 3.0.0 moved to multiple responses, restructured the output into classed objects, and made both filtering thresholds adaptive by default. Version 3.0.1 is the first release since that is purely repair.

◆ Where it's heading

The through-line is removing decisions the user was never well placed to make. Preference-order functions were renamed twice — first onto a metric-and-model scheme in 2.0.0, then onto a response-type scheme in 3.0.0 — and f_auto() picks one when none is given; target encoding went from automatic to opt-in; max_cor and max_vif now default to NULL and trigger a data-driven threshold derived from the 75th percentile of pairwise correlations through a sigmoid and a fitted correlation-to-VIF mapping. Each change is defensible and each one broke callers, which is the cost of this approach.

◆ Prediction

3.0.1 moved the example datasets out into a separate spatialData package and fixed four crashes rather than adding anything, so the next release is most likely more consolidation on the 3.0 surface than a fourth interface.

M
minimaxapprox
ANALYTICS
0.0

minimaxapprox found its Remez exchange had been silently returning the wrong minimax.

◆ Current state

minimaxapprox computes minimax polynomial and rational approximations to functions via the Remez exchange algorithm. Version 0.6.0 is a correctness release of unusual depth: the exchange was redesigned after two structural defects were found that let it converge to a reference-local fixed point that is not the global minimax, with no warning. The worked example in the notes has atan on [0,3] at degree 3 reporting an expected error 1.8 times smaller than the returned approximation's true maximum error.

◆ Where it's heading

The redesign changes what drives the algorithm: roots are now located across the whole interval from an oversampled grid rather than only between consecutive reference points, each sign region takes its search direction from the error's own sign instead of a mechanically alternated schedule, and endpoints are always retained as exchange candidates. Around that sit a cluster of fixes with the same signature — a stagnation check tested in only one direction, a missing abs() in a coefficient test, ztol applied on the monomial scale when Chebyshev was requested — each one a silent wrong answer rather than a crash. Conditioning on ranges far from [-1,1] was also addressed, at the cost of a breaking change.

◆ Prediction

Nearly every fix here came from auditing places where the package trusted its own structure instead of measuring the error curve, so the next release most likely continues that audit into the rational-approximation path rather than adding features.

Alternatives to collinear and minimaxapprox

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 collinear or minimaxapprox.

See all collinear alternatives → · See all minimaxapprox alternatives →

Recent activity from collinear and minimaxapprox

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agominimaxapproxCRAN release v0.6.0
  2. 3mo agocollinearNamespace, NA and sf fixes; example data moves to spatialData
  3. 8mo agocollinearAdaptive thresholds, multi-response support and a new output class
  4. 1y agocollinearCategorical responses, f_auto() defaults and future-based parallelism
  5. 2y agominimaxapproxCRAN release 0.2.0

Frequently asked questions

What is the difference between collinear and minimaxapprox?

Both compete on the same themes — r package — within Analytics. collinear and minimaxapprox 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 collinear better than minimaxapprox?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. collinear and minimaxapprox 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 collinear?

Top collinear alternatives in Analytics are ranked by recent ship velocity. Browse the "collinear alternatives" section above for the current picks, or visit /alternatives/collinear for the full list with editorial commentary on each.

What are the best alternatives to minimaxapprox?

Top minimaxapprox alternatives in Analytics are ranked by recent ship velocity. Browse the "minimaxapprox alternatives" section above for the current picks, or visit /alternatives/minimaxapprox for the full list with editorial commentary on each.