fmrs
Bioc currentVariable Selection in Finite Mixture of AFT Regression and FMR Models
Release Lineage
Entered 3.12 · Oct 28, 2020
Current · Requires R 4.6
Description
The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
40 0 exported
Complexity
2.1 avg / 11 max
Call network
40 nodes / 67 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
9,304
Files
36
Compiled share
57.2%
Has compiled src
Yes
Language breakdown
API
Exported functions
1
Internal functions
16
Testing & CI
Has tests
Yes
Test-to-code ratio
0.02
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.3.0
System requirements
–
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
12
First release
2020-10-27
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 100%
- Documented parameters
- not tracked
- Return-value docs
- 0%
- References docs
- 16%
Topics
Depended on by (1)
CRAN (1)
People
- Farhad Shokoohi author maintainer
Cite
Cite this package
Start here. This is the citation for the package itself.
citation("fmrs")Bioconductor packages have no CRAN DOI. The package landing page is https://bioconductor.org/packages/fmrs.
BibTeX, derived from DESCRIPTION
@Manual{fmrs,
title = {fmrs: Variable Selection in Finite Mixture of AFT Regression and FMR
Models},
author = {Shokoohi, Farhad},
year = {2026},
note = {R package version 1.22.0},
url = {https://bioconductor.org/packages/fmrs}
}Derived from the package DESCRIPTION, not from a citation file the authors wrote. If they publish one later, prefer it.
This is the citation for the package. It is not a citation for the R Observatory.
Cite this page
Use this when the claim is about a measurement on this page.
BibTeX
@misc{robservatoryfmrs,
author = {Balamuta, James Joseph},
title = {{R} {Observatory}: Metrics for {fmrs} version 1.22.0},
year = {2026},
publisher = {HJJB, LLC},
url = {https://r-observatory.thecoatlessprofessor.com/bioc/fmrs},
note = {Data set. Data release v2026-08-05}
}APA
Balamuta, J. J. (2026). R Observatory: Metrics for fmrs version 1.22.0 [Data set]. HJJB, LLC. Data release v2026-08-05. https://r-observatory.thecoatlessprofessor.com/bioc/fmrsRIS
TY - DATA
AU - Balamuta, James Joseph
TI - R Observatory: Metrics for fmrs version 1.22.0
PY - 2026
PB - HJJB, LLC
N1 - Data release v2026-08-05
UR - https://r-observatory.thecoatlessprofessor.com/bioc/fmrs
ER - In prose
These package metrics were obtained from the R Observatory (Balamuta, 2026), data release v2026-08-05, https://r-observatory.thecoatlessprofessor.com/bioc/fmrs.Bound to data release v2026-08-05, which is what makes the numbers on this page reproducible. See how to cite.