Package: mccount 0.1.1.9000

mccount: Estimate Recurrent Event Burden with Competing Risks

Calculates mean cumulative count (MCC) to estimate the expected cumulative number of recurrent events per person over time in the presence of competing risks and censoring. Implements both the Dong-Yasui equation method and sum of cumulative incidence method described in Dong, et al. (2015) <doi:10.1093/aje/kwu289>. Supports inverse probability weighting for causal inference as outlined in Gaber, et al. (2023) <doi:10.1093/aje/kwad031>. Provides S3 methods for printing, summarizing, plotting, and extracting results. Handles grouped analyses and integrates with 'ggplot2' <https://ggplot2.tidyverse.org/> for visualization.

Authors:Kenneth A. Taylor [aut, cre, cph]

mccount_0.1.1.9000.tar.gz
mccount_0.1.1.9000.zip(r-4.7-any)mccount_0.1.1.9000.zip(r-4.6-any)mccount_0.1.1.9000.zip(r-4.5-any)
mccount_0.1.1.9000.tgz(r-4.6-any)mccount_0.1.1.9000.tgz(r-4.5-any)
mccount_0.1.1.9000.tar.gz(r-4.7-any)mccount_0.1.1.9000.tar.gz(r-4.6-any)
mccount_0.1.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mccount/json (API)

# Install 'mccount' in R:
install.packages('mccount', repos = c('https://kennethataylor.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kennethataylor/mccount/issues

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

On CRAN:

Conda:

biostatisticscensoring-eventscompeting-risksepidemiologymean-cumulative-countmean-cumulative-functionrecurrent-events

4.90 score 2 stars 5 scripts 236 downloads 14 exports 26 dependencies

Last updated from:e2222cbf5c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK183
source / vignettesOK210
linux-release-x86_64OK176
macos-release-arm64OK106
macos-oldrel-arm64OK123
windows-develOK125
windows-releaseOK119
windows-oldrelOK139
wasm-releaseOK130

Exports:as_mcccompare_mccfilter_mccgeom_line_mccis_groupedis_mccis_weightedmccmcc_detailsmcc_estimatesmcc_final_valuesmcc_grouping_varmcc_groupsmcc_method

Dependencies:cardsclicmprskcpp11data.tabledplyrgenericsgluelatticelifecyclemagrittrMatrixpillarpkgconfigpurrrR6rlangstringistringrsurvivaltibbletidyrtidyselectutf8vctrswithr

Estimating MCC After Matching or Weighting
Introduction | When to Use Weights with MCC | Inverse Probability of Treatment Weighting (IPTW) | Step 1: Estimate Propensity Scores and Create Weights | Step 2: Estimate Weighted MCC | Step 3: Compare Weighted vs Unweighted Results | Propensity Score Matching | Important Note on Matching Weights | Example: 1:1 Nearest Neighbor Matching (No Weights Needed) | Example: Full Matching with Weights | Visualization of Weighted/Matched Results | Key Interpretation Points | Causal vs Descriptive Interpretation | Assumptions | Stabilized vs Unstabilized Weights | Not Covered | Summary
Rendered fromestimating-mcc-after-matching-or-weighting.Rmdusingknitr::rmarkdown

Last update: 2025-11-14
Started: 2025-11-04

Choosing Between Methods
Two Approaches, Same Goal | When Methods Are Equivalent | Administrative Censoring Only | Synchronized Event-Interval Censoring | When Methods Differ and Why | When Events Are Exchangeable | When Event Order Matters | Documenting Your Choice | Special Case - Delayed Study Entry | Summary
Rendered fromchoosing-between-methods.Rmdusingknitr::rmarkdown

Last update: 2025-09-04
Started: 2025-09-04