DYJtrans

Evaluates the derivative of the Yeo-Johnson transformation at
the provided argument.

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

DYJtrans function

<dl><dt>y</dt>
<dd>The argument to be supplied to the derivative of the Yeo-Johnson
transformation.</dd>
<dt>theta</dt>
<dd>The parameter of the Yeo-Johnson transformation. This should be
a number in the range [0,2].</dd></dl>

Arguments

Derivative of the Yeo-Johnson transformation function — DYJtrans

<dl>

<dt>y</dt>
<dd>The argument to be supplied to the derivative of the Yeo-Johnson
transformation.</dd>


<dt>theta</dt>
<dd>The parameter of the Yeo-Johnson transformation. This should be
a number in the range [0,2].</dd>

</dl>

DYJtrans: Derivative of the Yeo-Johnson transformation function

Description

Usage

Value

Arguments