Computes the Nadaraya-Watson weights.

Provides estimates for the bivariate and trivariate distribution
functions and bivariate and trivariate survival functions for
censored gap times. Two approaches, using existing methodologies,
are considered: (i) the Lin's estimator, which is based on the
extension the Kaplan-Meier estimator of the distribution function
for the first event time and the Inverse Probability of Censoring
Weights for the second time (Lin DY, Sun W, Ying Z (1999)
<doi:10.1093/biomet/86.1.59> and (ii) another estimator
based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008)
<https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>).
The proposed methods are the landmark estimators based on
subsampling approach, and the estimator based on weighted cumulative
hazard estimator. The package also provides nonparametric estimator
conditional to a given continuous covariate. All these methods have
been submitted to be published.

Gustavo Soutinho

survivalREC

Nonparametric Estimation of the Distribution of Gap Times for
Recurrent Events

Luis Meira-Machado

Artur Araujo

Ana Moreira

NWW function

<dl><dt>covariate</dt>
<dd>Covariate values for obtaining weights.</dd>
<dt>x</dt>
<dd>Covariate value to compute the weight at.</dd>
<dt>kernel</dt>
<dd>A character string specifying the desired kernel. See details 
below for possible options. 
Defaults to "gaussian" where the gaussian density kernel will be used.</dd>
<dt>bw</dt>
<dd>A single numeric value to compute a kernel density bandwidth.</dd></dl>

Arguments

Gustavo Soutinho and Luis Meira-Machado

Author

Nadaraya-Watson weights — NWW

<dl>

<dt>covariate</dt>
<dd>Covariate values for obtaining weights.</dd>


<dt>x</dt>
<dd>Covariate value to compute the weight at.</dd>


<dt>kernel</dt>
<dd>A character string specifying the desired kernel. See details 
below for possible options. 
Defaults to "gaussian" where the gaussian density kernel will be used.</dd>


<dt>bw</dt>
<dd>A single numeric value to compute a kernel density bandwidth.</dd>

</dl>

NWW: Nadaraya-Watson weights

Description

Usage

Value

Arguments

Author

Details

Examples