age.sex.zip | Simulate CRC data with age, sex, and zip code |
AICc.vgam | Compute the AICc for a VGAM model |
apply.ic.fit | Select an LLLM at each point |
apply.local.ml | Fit LLLMs |
as.num | Conversion to numeric |
captures | Simulating captures |
construct.vgam | Make a VGAM model |
extract.CI | Use bootstrap output to get CI |
flat.IC | Select an LLM |
flat.log.linear | Fit an LLM |
formatdata | Format the CRC data |
french.1 | A fake dataset, french.1 |
get.IC | Compute an information criterion |
ic.all | Compute an IC for several LLMs |
ic.fit | Select and fit an LLM |
ic.wghts | Information criterion model weights |
initialize.u.vec | Initialize log-linear parameters |
init.pop | Set up a fake population |
llcrc.flat.boots | Bootstrapping LLMs |
lllcrc | Local log-linear models (LLLMs) for capture-recapture (CRC) |
lllcrc.boots | Bootstrap for LLLMs |
lllcrc-package | Local Log-linear Models for Capture-Recapture |
llm.sim | Simulate basic log-linear CRC experiments |
local.ml | Maximum likelihood estimation for fixed LLLMs |
make.design.matrix | Construct standard LLM design matrix. |
make.hierarchical.term.sets | Generate a universe of hierarchical models. |
make.patterns.template | Template for capture-pattern counts |
micro.post.stratify | Collapse CRC data through micro post-stratification |
odd.evens | Determine the even-ness of capture patterns |
patterns | Collapse capture events into capture patterns (strings) |
patterns.possible | Generate all observable capture patterns |
pirls | Maximum likelihood for log-linear coefficients |
plot | Plot LLLMs |
plot.llsim | Plot the output of 'llm.sim' |
pop.to.counts | Put CRC data into LLM vector |
poptop | Simulate a CRC experiment |
rates.by.category | Display estimated rates of missingness by category |
resample.captures | Tool for bootstrapping |
saturated.local | Use odd-even formula to fit saturated LLM |
smooth.patterns | Local averaging for LLLMs |
stackydens | Stack local capture pattern frequencies for plotting |
string.to.array | Put LLM vector into a LLM design matrix |
summarize.by.factors | Summarize LLLM by factor |
summary | Summary of LLLM or VGAM CRC analysis |
vgam.crc | Build a VGAM CRC model |
vgam.crc.boots | Bootstrapping for a VGAM CRC model |
y.string.to.y.glm | Capture patterns to design matrix |
zglm | Maximum likelihood for log-linear coefficients |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.