#robuststatistics — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #robuststatistics, aggregated by home.social.
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robscale 0.5.3 is now on CRAN: A major update since the last public release (0.2.1).
New: 11 robust estimators with confidence intervals, plus a variance-weighted ensemble combining seven scale statistics via bootstrap. Newton–Raphson replaces scoring iteration (2–4 steps vs 6–8), a fused AVX2 kernel does NR accumulation in one data pass, and all input validation runs in C++ with zero R-side allocation.
Speedups vs existing implementations:
Small samples (n ≤ 20):
• robScale/robLoc: 4–5× vs revss 3.1.0
• Qn: 6× vs robustbase
• MAD: 21–26× vs stats::mad
• IQR: 37× vs stats::IQRMid-to-large samples (n ≥ 1000):
• robScale: 2–4× vs revss
• Sn: 7–9× vs robustbase
• MAD: 5–8× vs stats::mad
• IQR: 5–7× vs stats::IQR
• GMD: 2–8× vs GiniDistancehttps://cran.r-project.org/package=robscale
https://github.com/davdittrich/robscale#RStats #Statistics #RobustStatistics #CRAN #DataScience #OpenSource
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robscale 0.5.3 is now on CRAN: A major update since the last public release (0.2.1).
New: 11 robust estimators with confidence intervals, plus a variance-weighted ensemble combining seven scale statistics via bootstrap. Newton–Raphson replaces scoring iteration (2–4 steps vs 6–8), a fused AVX2 kernel does NR accumulation in one data pass, and all input validation runs in C++ with zero R-side allocation.
Speedups vs existing implementations:
Small samples (n ≤ 20):
• robScale/robLoc: 4–5× vs revss 3.1.0
• Qn: 6× vs robustbase
• MAD: 21–26× vs stats::mad
• IQR: 37× vs stats::IQRMid-to-large samples (n ≥ 1000):
• robScale: 2–4× vs revss
• Sn: 7–9× vs robustbase
• MAD: 5–8× vs stats::mad
• IQR: 5–7× vs stats::IQR
• GMD: 2–8× vs GiniDistancehttps://cran.r-project.org/package=robscale
https://github.com/davdittrich/robscale#RStats #Statistics #RobustStatistics #CRAN #DataScience #OpenSource
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Robust estimation demands highly efficient computation, especially in streaming anomaly detection where latency budgets are tight.
While Rousseeuw & Croux's robust estimators ($Q_n$ and $S_n$), and Rousseeuw & Verboven's M-estimators of location and scale for very small samples, provide exceptional reliability, computing them requires intensive math.
robscale 0.1.5 is now on CRAN. It delivers a native C++17/Rcpp implementation designed for absolute speed. The package utilizes SIMD-vectorized $\tanh$ evaluation, Newton-Raphson iteration, and optimal sorting networks for cache-aware median selection.
The result? A 1.6x up to ~28x performance leap over pure-R implementations. The mathematical results remain identical; only the computational underpinnings change.
📦 CRAN: https://cran.r-project.org/package=robscale
💻 Code: https://github.com/davdittrich/robscale -
Robust estimation demands highly efficient computation, especially in streaming anomaly detection where latency budgets are tight.
While Rousseeuw & Croux's robust estimators ($Q_n$ and $S_n$), and Rousseeuw & Verboven's M-estimators of location and scale for very small samples, provide exceptional reliability, computing them requires intensive math.
robscale 0.1.5 is now on CRAN. It delivers a native C++17/Rcpp implementation designed for absolute speed. The package utilizes SIMD-vectorized $\tanh$ evaluation, Newton-Raphson iteration, and optimal sorting networks for cache-aware median selection.
The result? A 1.6x up to ~28x performance leap over pure-R implementations. The mathematical results remain identical; only the computational underpinnings change.
📦 CRAN: https://cran.r-project.org/package=robscale
💻 Code: https://github.com/davdittrich/robscale