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lllcrc (version 1.1)

Local Log-linear Models for Capture-Recapture

Description

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.

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Version

Install

install.packages('lllcrc')

Monthly Downloads

5

Version

1.1

License

GPL-2

Maintainer

Zach Kurtz

Last Published

March 12th, 2014

Functions in lllcrc (1.1)

construct.vgam

Make a VGAM model
apply.local.ml

Fit LLLMs
lllcrc-package

Local Log-linear Models for Capture-Recapture
apply.ic.fit

Select an LLLM at each point
init.pop

Set up a fake population
ic.wghts

Information criterion model weights
as.num

Conversion to numeric
flat.log.linear

Fit an LLM
get.IC

Compute an information criterion
local.ml

Maximum likelihood estimation for fixed LLLMs
initialize.u.vec

Initialize log-linear parameters
poptop

Simulate a CRC experiment
llcrc.flat.boots

Bootstrapping LLMs
zglm

Maximum likelihood for log-linear coefficients
extract.CI

Use bootstrap output to get CI
french.1

A fake dataset, french.1
odd.evens

Determine the even-ness of capture patterns
AICc.vgam

Compute the AICc for a VGAM model
resample.captures

Tool for bootstrapping
patterns.possible

Generate all observable capture patterns
lllcrc.boots

Bootstrap for LLLMs
vgam.crc.boots

Bootstrapping for a VGAM CRC model
captures

Simulating captures
plot.llsim

saturated.local

Use odd-even formula to fit saturated LLM
make.hierarchical.term.sets

Generate a universe of hierarchical models.
make.design.matrix

Construct standard LLM design matrix.
ic.fit

Select and fit an LLM
formatdata

Format the CRC data
vgam.crc

Build a VGAM CRC model
string.to.array

Put LLM vector into a LLM design matrix
stackydens

Stack local capture pattern frequencies for plotting
summary.lllcrc

Summary of LLLM or VGAM CRC analysis
lllcrc

Local log-linear models (LLLMs) for capture-recapture (CRC)
pop.to.counts

Put CRC data into LLM vector
ic.all

Compute an IC for several LLMs
summarize.by.factors

Summarize LLLM by factor
pirls

Maximum likelihood for log-linear coefficients
patterns

Collapse capture events into capture patterns (strings)
flat.IC

Select an LLM
llm.sim

Simulate basic log-linear CRC experiments
smooth.patterns

Local averaging for LLLMs
make.patterns.template

Template for capture-pattern counts
plot.lllcrc

Plot LLLMs
age.sex.zip

Simulate CRC data with age, sex, and zip code
micro.post.stratify

Collapse CRC data through micro post-stratification
y.string.to.y.glm

Capture patterns to design matrix
rates.by.category

Display estimated rates of missingness by category