Package index

Examining the extent of irregularity

Functions for examining the extent of irregularity in observation times

abacus.plot()

Create an abacus plot Creates an abacus plot, depicting visits per subject over time

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

Using inverse-intensity weighting

Using inverse-intensity weighting to account for irregular and informative observation times

iiw.weights()

Compute inverse-intensity weights.

iiwgee()

Fit an inverse-intensity weighted GEE.

iiw()

Given a proportional hazards model for visit intensities, compute inverse-intensity weights.

Multiple Outputation

Functions for performing multiple outputation

mo()

Multiple outputation for longitudinal data subject to irregular observation.

outputation()

Create an outputted dataset for use with multiple outputation.

Semi-parametric joint model

Functions for fitting the Liang semi-parametric joint model

Liang()

Fit a semi-parametric joint model

Liangint()

Fit a semi-parametric joint model, incorporating intercept estimation

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.

Low-level functions

Functions for constructing the inverse-intensity weights manually

addcensoredrows()

Add rows corresponding to censoring times to a longitudinal dataset

lagfn()

Create lagged versions the variables in data