gs.interpolation

This function implements the interpolation search algorithm,
that starts from a given feasible point and looks in the given direction for
the root of the violation curve.

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

gs.interpolation function

<dl><dt>evaluations</dt>
<dd>Matrix of evaluated test statistics and critical values.</dd>
<dt>dir</dt>
<dd>Search direction.</dd>
<dt>iter.nbr</dt>
<dd>Iteration number.</dd>
<dt>hp</dt>
<dd>List of hyperparameters.</dd></dl>

Arguments

Return the next point to evaluate when doing interpolation search — gs.interpolation

<dl>

<dt>evaluations</dt>
<dd>Matrix of evaluated test statistics and critical values.</dd>


<dt>dir</dt>
<dd>Search direction.</dd>


<dt>iter.nbr</dt>
<dd>Iteration number.</dd>


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

</dl>

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gs.interpolation: Return the next point to evaluate when doing interpolation search

Description

Usage

Value

Arguments