**Measures, Tests and Removes Multivariate Skewness** (**MultiSkew**)

Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.

**Efficiently Generates Random Order Statistic Variables** (**orderstats**)

All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.

**Some Helper Functions that Help Create Features from Data** (**featurizer**)

A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.

**Analysis of Intra Annual Density Fluctuations** (**iadf**)

Calculate false ring proportions from data frames of intra annual density fluctuations.

**Recursive Partitioning Survival Trees** (**rpst**)

An implementation of Recursive Partitioning Survival Trees via a node-splitting rule that builds decision tree models that reflected within-node and within-treatment responses. The algorithm aims to find the maximal difference in survival time among different treatments.

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