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Provides a suite of methods for detecting influential subjects in longitudinal datasets, particularly when observations occur at irregular time points. The methods identify individuals whose response trajectories deviate significantly from the population pattern, enabling detection of anomalies or subjects exerting undue influence on model outcomes.
| Version: | 0.1.0 |
| Depends: | R (≥ 4.1.0) |
| Imports: | dplyr, corpcor, future, future.apply, glmnet, mstate, numDeriv, penalized, progress, progressr, survival |
| Suggests: | ggplot2, rlang, mice |
| Published: | 2025-11-26 |
| DOI: | 10.32614/CRAN.package.flassomsm (may not be active yet) |
| Author: | Atanu Bhattacharjee [aut, cre, ctb], Gajendra Kumar Vishwakarma [aut, ctb], Abhipsa Tripathy [aut, ctb] |
| Maintainer: | Atanu Bhattacharjee <atanustat at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | flassomsm results |
| Reference manual: | flassomsm.html , flassomsm.pdf |
| Package source: | flassomsm_0.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): flassomsm_0.1.0.tgz, r-oldrel (arm64): flassomsm_0.1.0.tgz, r-release (x86_64): flassomsm_0.1.0.tgz, r-oldrel (x86_64): flassomsm_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.