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osktnorm - A Moment-Targeting Normality Transformation Based on Tukey g-h Distribution

Implements a moment-targeting normality transformation based on the simultaneous optimization of Tukey g-h distribution parameters. The method is designed to minimize both asymmetry (skewness) and excess peakedness (kurtosis) in non-normal data by mapping it to a standard normal distribution (Cebeci et al., 2026 <doi:10.3390/sym18030458>). Optimization is performed by minimizing an objective function derived from the Anderson-Darling goodness-of-fit statistic with Stephens's correction factor, utilizing the L-BFGS-B algorithm for robust parameter estimation. This approach provides an effective alternative to power transformations like Box-Cox and Yeo-Johnson, particularly for data requiring precise tail-behavior adjustment.

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cpp

4.48 score 307 downloads

odetector - Outlier Detection Using Partitioning Clustering Algorithms

An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.

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anomaly-detectioncluster-analysisclusteringclustering-methodsdatadatapreparationdatapreprocessingexception-handlingfcmfraud-detectionfuzzy-clusteringnovelty-detectionoutlier-detectionoutlier-removaloutlierspartitioningpcmsurprise-exploration

3.70 score 5 scripts 215 downloads

fastrda - Fast Redundancy Analysis (RDA) with High-Performance 'C++' Backend

Provides a high-performance implementation of redundancy analysis (RDA) in 'C++' using 'Armadillo' and 'OpenMP'. Supports standard and partial RDA, centering, scaling, overall and axis-wise permutation tests, biplot visualization, score extraction, and prediction. Designed for large ecological, genomic, and other multivariate data sets where computational speed and memory efficiency are required.

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openblascppopenmp

3.00 score 4 scripts 302 downloads