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priorityelasticnet (version 0.2.0)

Comprehensive Analysis of Multi-Omics Data Using an Offset-Based Method

Description

Priority-ElasticNet extends the Priority-LASSO method (Klau et al. (2018) ) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets.

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Install

install.packages('priorityelasticnet')

Monthly Downloads

162

Version

0.2.0

License

GPL-3

Maintainer

Laila Qadir Musib

Last Published

January 19th, 2025

Functions in priorityelasticnet (0.2.0)

Pen_Data

Simulated Patient Data for Binary Classification
cvm_priorityelasticnet

priorityelasticnet with several block specifications
predict.priorityelasticnet

Predictions from priorityelasticnet
coef.priorityelasticnet

Extract coefficients from a priorityelasticnet object
weightedThreshold

A Shiny App for Model Evaluation and Weighted Threshold Optimization
missing.control

Construct control structures for handling of missing data for priorityelasticnet
compare_boolean

Compare the rows of a matrix with a pattern
priorityelasticnet

Priority Elastic Net for High-Dimensional Data
calculate_offsets

Calculates the offsets for the current block