R: Analysis of Longitudinal Data with Irregular Observation Times

# Analysis of Longitudinal Data with Irregular Observation Times

## Documentation for package ‘IrregLong’ version 0.4.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

abacus.plot | Create an abacus plot Creates an abacus plot, depicting visits per subject over time
addcensoredrows | Add rows corresponding to censoring times to a longitudinal dataset
create.bootstrapped.dataset | Create a single bootstrap sample for clustered data For clustered data, create a bootstrapped sample by sampling, with replacement, the same number of clusters as in the original dataset.
extent.of.irregularity | Measures of extent of visit irregularity Provides visual and numeric measures of the extent of irregularity in observation times in a longitudinal dataset
iiw | Given a proportional hazards model for visit intensities, compute inverse-intensity weights.
iiw.weights | Compute inverse-intensity weights.
iiwgee | Fit an inverse-intensity weighted GEE.
lagfn | Create lagged versions the variables in data
Liang | Fit a semi-parametric joint model
Liangint | Fit a semi-parametric joint model, incorporating intercept estimation
mo | Multiple outputation for longitudinal data subject to irregular observation.
outputation | Create an outputted dataset for use with multiple outputation.