Package: IrregLong 0.4.1

IrregLong: Analysis of Longitudinal Data with Irregular Observation Times

Functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi:10.1111/j.1541-0420.2008.01104.x>).

Authors:Eleanor Pullenayegum [aut, cre]

IrregLong_0.4.1.tar.gz
IrregLong_0.4.1.zip(r-4.7-any)IrregLong_0.4.1.zip(r-4.6-any)IrregLong_0.4.1.zip(r-4.5-any)
IrregLong_0.4.1.tgz(r-4.6-any)IrregLong_0.4.1.tgz(r-4.5-any)
IrregLong_0.4.1.tar.gz(r-4.7-any)IrregLong_0.4.1.tar.gz(r-4.6-any)
IrregLong_0.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
IrregLong/json (API)

# Install 'IrregLong' in R:

install.packages('IrregLong', repos = c('https://epullenayegum.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/epullenayegum/irreglong/issues

Pkgdown/docs site:https://epullenayegum.github.io

On CRAN:

4.62 score 3 stars 14 scripts 399 downloads 1 mentions 11 exports 28 dependencies

Last updated from:ac13184f6f. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	162
source / vignettes	OK	205
linux-release	OK	157
macos-release	OK	165
macos-oldrel	OK	225
windows-devel	OK	104
windows-release	OK	129
windows-oldrel	OK	104
wasm-release	OK	108

Exports:abacus.plot addcensoredrows extent.of.irregularity iiw iiw.weights iiwgee lagfn Liang Liangint mo outputation

Dependencies:backports broom cli cpp11 data.table dplyr geepack generics glue lattice lifecycle magrittr MASS Matrix pillar pkgconfig purrr R6 rlang stringi stringr survival tibble tidyr tidyselect utf8 vctrs withr

Analysis of longitudinal data with irregular observation times

Rendered fromIrreglong-vignette.Rmdusingknitr::rmarkdown

Last update: 2026-01-28
Started: 2022-02-10

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

Package: IrregLong 0.4.1

IrregLong: Analysis of Longitudinal Data with Irregular Observation Times

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