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sicure (version 0.1.1)

Single-Index Mixture Cure Models

Description

Single-index mixture cure models allow estimating the probability of cure and the latency depending on a vector (or functional) covariate, avoiding the curse of dimensionality. The vector of parameters that defines the model can be estimated by maximum likelihood. A nonparametric estimator for the conditional density of the susceptible population is provided. For more details, see Piñeiro-Lamas (2024) (). Funding: This work, integrated into the framework of PERTE for Vanguard Health, has been co-financed by the Spanish Ministry of Science, Innovation and Universities with funds from the European Union NextGenerationEU, from the Recovery, Transformation and Resilience Plan (PRTR-C17.I1) and from the Autonomous Community of Galicia within the framework of the Biotechnology Plan Applied to Health.

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Version

Install

install.packages('sicure')

Monthly Downloads

120

Version

0.1.1

License

GPL (>= 2)

Maintainer

Beatriz Piñeiro-Lamas

Last Published

April 21st, 2025

Functions in sicure (0.1.1)

loglik.simcm

Logarithm of the likelihood of a single-index mixture cure model
sicure.f

Estimation of the vector of parameters in a single-index mixture cure model with a functional covariate
fun.opt

Objective function
cd.uncured

Cross-validation conditional density of the susceptible population
probcure.sm

Smoothed version of the nonparametric estimator of the conditional probability of cure
k.epa

Rescaled Epanechnikov kernel
cd.sm.uncured

Smoothed cross-validation conditional density estimator of the susceptible population
sicure.v

Estimation of the vector of parameters in a single-index mixture cure model with a vector of covariates
sicure.vf

Estimation of the vector of parameters in a single-index mixture cure model with a vector and a functional covariate
kNN.Mahalanobis

K Nearest Neighbors with Mahalanobis Distance