design

Extract design matrix from data.frame and formula

Various methods for targeted and semiparametric inference including
augmented inverse probability weighted (AIPW) estimators for missing data and
causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
variable importance and conditional average treatment effects (CATE)
(van der Laan (2006) <doi:10.2202/1557-4679.1008>),
estimators for risk differences and relative risks (Richardson et al. (2017)
<doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Klaus Holst

targeted

Targeted Inference

Klaus K. Holst

Andreas Nordland

design function

<dl><dt>formula</dt>
<dd>formula</dd>
<dt>data</dt>
<dd>data.frame</dd>
<dt>intercept</dt>
<dd>If FALSE (default) an intercept is not included</dd>
<dt>rm_envir</dt>
<dd>Remove environment</dd>
<dt>...</dt>
<dd>additional arguments (e.g, specials such weights, offsets,
subset)</dd></dl>

Arguments

Author

Extract design matrix — design

<dl>

<dt>formula</dt>
<dd>formula</dd>


<dt>data</dt>
<dd>data.frame</dd>


<dt>intercept</dt>
<dd>If FALSE (default) an intercept is not included</dd>


<dt>rm_envir</dt>
<dd>Remove environment</dd>


<dt>...</dt>
<dd>additional arguments (e.g, specials such weights, offsets,
subset)</dd>

</dl>

design: Extract design matrix

Description

Usage

Value

Arguments

Author