A_step

This function performs the approximation step in the EAM
algorithm. More specifically, it fits a Gaussian-process regression model
(Kriging) to the evaluated data points \((\theta, c(\theta))\).

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

A_step function

<dl><dt>evaluations</dt>
<dd>Matrix containing each point that was already evaluated,
alongside the corresponding test statistic and critical value, as its rows.</dd>
<dt>verbose</dt>
<dd>Verbosity parameter.</dd></dl>

Arguments

A-step in the EAM algorithm described in KMS19 — A_step

<dl>

<dt>evaluations</dt>
<dd>Matrix containing each point that was already evaluated,
alongside the corresponding test statistic and critical value, as its rows.</dd>


<dt>verbose</dt>
<dd>Verbosity parameter.</dd>

</dl>

A_step: A-step in the EAM algorithm described in KMS19

Description

Usage

Value

Arguments

See Also