Package: ctgimme 0.1.0

ctgimme: Continuous-Time Subgrouping with GIMME

Estimates group-, subgroup-, and individual-level dynamic structures from multivariate intensive longitudinal data using continuous-time state-space models. The subgrouping procedure combines iterative shared-path searches with recurrent-evidence feature screening and partitioning around medoids. The continuous-time group iterative multiple model estimation method is described in Park et al. (2025) <doi:10.1080/10705511.2024.2429544>.

Authors:Jonathan J. Park [aut, cre, cph], Nathan Xin Mills [aut, ctb]

ctgimme_0.1.0.tar.gz
ctgimme_0.1.0.zip(r-4.7-any)ctgimme_0.1.0.zip(r-4.6-any)ctgimme_0.1.0.zip(r-4.5-any)
ctgimme_0.1.0.tgz(r-4.6-any)ctgimme_0.1.0.tgz(r-4.5-any)
ctgimme_0.1.0.tar.gz(r-4.7-any)ctgimme_0.1.0.tar.gz(r-4.6-any)
ctgimme_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ctgimme/json (API)

# Install 'ctgimme' in R:

install.packages('ctgimme', repos = c('https://jpark93.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jpark93/ctgimme/issues

On CRAN:

3.85 score 2 stars 6 scripts 402 downloads 2 exports 89 dependencies

Last updated from:41ed29a28c. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	200
source / vignettes	OK	210
linux-release	OK	171
macos-release	OK	201
macos-oldrel	OK	201
windows-devel	OK	144
windows-release	OK	147
windows-oldrel	OK	144
wasm-release	OK	138