LambertW - Probabilistic Models to Analyze and Gaussianize Heavy-Tailed, Skewed Data
Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away.
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gaussianizegaussianize-dataheavy-tailedheavy-tailed-distributionsleptokurtosisnormal-distributionnormalizationskewed-datastatisticscpp
8.65 score 10 stars 13 dependents 107 scripts 7.2k downloadsForeCA - Forecastable Component Analysis
Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency explicitly into account and searches for the most ''forecastable'' signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.
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blind-source-separationdimensionality-reductionforecastingmultivariate-timeseriessignal-processingspectrumtime-seriestime-series-analysis
5.53 score 16 stars 42 scripts 395 downloads