smooth.patterns

The capture-recapture data in the form that is
  returned by <code><a href="/link/formatdata?package=lllcrc&version=1.1" rd-options="" data-mini-rdoc="lllcrc::formatdata">formatdata</a></code> or
  <code><a href="/link/micro.post.stratify?package=lllcrc&version=1.1" rd-options="" data-mini-rdoc="lllcrc::micro.post.stratify">micro.post.stratify</a></code>.

The approximate fraction of the data that is
  included in the support of the kernel for the local
  averages.

kfrac

A matrix a single column, with rownames that
  match the covariate names in <code>dat</code>.  The values in
  the column are scalars that are used in constructing
  distances between covariate vectors.  Raw differences are
  divided by the corresponding 

A nearest-neighbors procedure is used in conjunction with
the Epanechnikov kernel to define a kernel smooth of
multinomial outcomes across the covariate space

Applies local log-linear capture-recapture models (LLLMs) a for
    closed populations, as described in the ongoing doctoral thesis work of
    Zachary Kurtz in the department of statistics at Carnegie Mellon
    University.  The method is relevant when there are 3-4 capture occasions,
    with auxiliary covariates available for all capture occasions.  As part of
    estimating the number of missing population units, the method estimates the
    "rate of missingness" as it varies over the covariate space.  In addition,
    user-friendly functions are provided to recreate (approximately) the method
    of Zwane and Van der Heijden (2004), which applied the VGAM package in a
    way that is closely related to LLLMs.

smooth.patterns: Local averaging for LLLMs

Description

Usage

Arguments

Value

Details

References