# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "NetSurvProx" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'NetSurvProx: ''NetSurvProx'': Network-Based Survival Analysis via Proximal
  Methods'
version: 1.0.0
abstract: Introduces a novel network-constrained survival analysis framework for variable
  selection and parameter estimation in penalized survival models with convex penalties.
  The package extends two classical survival models, the Cox Proportional Hazards
  (PH) model and the Accelerated Failure Time (AFT) model, by incorporating prior
  biological knowledge from curated interaction networks (e.g., KEGG) into a double-penalty
  framework. The first penalty enforces variable selection through a LASSO penalty,
  while the second preserves gene-gene correlations by incorporating Laplacian-based
  constraints, ensuring that biologically relevant network structures are maintained.
  Using censored survival data, the method enables the identification of predictive
  biomarkers and pathways with potential relevance for target therapies. Model estimation
  is performed via proximal optimization algorithms combined with cross-validation
  for reliable tuning. To enhance interpretability, dedicated utility functions are
  implemented to consolidate results, yielding biologically coherent insights that
  can support personalized medicine and contribute to improved patient outcomes.
authors:
- family-names: Mecchi
  given-names: Maura
  email: maura.mecchi@unibas.it
- family-names: Iuliano
  given-names: Antonella
  email: antonella.iuliano@unibas.it
repository: https://cran.r-universe.dev
commit: 210154a09bc18f64e8f6fed382a097a00743e889
date-released: '2026-06-09'
contact:
- family-names: Mecchi
  given-names: Maura
  email: maura.mecchi@unibas.it