R-universe search: topic:data-reduction

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easystats

parameters:Processing of Model Parameters

Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).

Maintained by Daniel Lüdecke. Last updated 10 days ago.

beta bootstrap ci confidence-intervals data-reduction easystats fa feature-extraction feature-reduction hacktoberfest parameters pca pvalues regression-models robust-statistics standardize standardized-estimates statistical-models

454 stars 15.67 score 1.8k scripts 56 dependents

uscbiostats

partition:Agglomerative Partitioning Framework for Dimension Reduction

A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>.

Maintained by Malcolm Barrett. Last updated 5 months ago.

data-reduction dimensionality-reduction partitional-clustering openblas cpp

36 stars 7.72 score 27 scripts 1 dependents