predict.density

Kernel density estimator predictions

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

predict.density function

<dl><dt>object</dt>
<dd>density object</dd>
<dt>xnew</dt>
<dd>New data on which to make predictions for</dd>
<dt>...</dt>
<dd>additional arguments to lower level functions</dd></dl>

Arguments

Author

Prediction for kernel density estimates — predict.density

<dl>

<dt>object</dt>
<dd>density object</dd>


<dt>xnew</dt>
<dd>New data on which to make predictions for</dd>


<dt>...</dt>
<dd>additional arguments to lower level functions</dd>

</dl>

predict.density: Prediction for kernel density estimates

Description

Usage

Arguments

Author