do.optimization.Mstep

This function finds the point for which the expected improvement
is optimal, based on a given set of starting values. (M_step.R)

Several statistical methods for analyzing survival data under various forms of dependent
censoring are implemented in the package. In addition to accounting for dependent censoring, it
offers tools to adjust for unmeasured confounding factors. The implemented approaches allow
users to estimate the dependency between survival time and dependent censoring time, based
solely on observed survival data. For more details on the methods, refer to Deresa and Van
Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023)
<doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>,
Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Rutten et al. (2024+)
<doi:10.48550/arXiv.2403.11860> and Ding and Van Keilegom (2024).

Negera Wakgari Deresa

depCensoring

Statistical Methods for Survival Data with Dependent Censoring

Ilias Willems

Gilles Crommen

Jie Ding

Claudia Czado

Ingrid Van Keilegom

do.optimization.Mstep function

<dl><dt>start.vals</dt>
<dd>Starting values for optimization.</dd>
<dt>EI.Mstep</dt>
<dd>Function to compute expected improvements.</dd>
<dt>hyperparams</dt>
<dd>List of hyperparameters.</dd></dl>

Arguments

Optimize the expected improvement — do.optimization.Mstep

<dl>

<dt>start.vals</dt>
<dd>Starting values for optimization.</dd>


<dt>EI.Mstep</dt>
<dd>Function to compute expected improvements.</dd>


<dt>hyperparams</dt>
<dd>List of hyperparameters.</dd>

</dl>

do.optimization.Mstep: Optimize the expected improvement

Description

Usage

Value

Arguments