Package: SCCS 1.7
"Yonas Ghebremichael Weldeselassie"
SCCS: The Self-Controlled Case Series Method
Various self-controlled case series models used to investigate associations between time-varying exposures such as vaccines or other drugs or non drug exposures and an adverse event can be fitted. Detailed information on the self-controlled case series method and its extensions with more examples can be found in Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y. (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies: A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) and <https://sccs-studies.info/index.html>.
Authors:
SCCS_1.7.tar.gz
SCCS_1.7.tar.gz(r-4.5-noble)SCCS_1.7.tar.gz(r-4.4-noble)
SCCS_1.7.tgz(r-4.4-emscripten)SCCS_1.7.tgz(r-4.3-emscripten)
SCCS.pdf |SCCS.html✨
SCCS/json (API)
| # Install 'SCCS' in R: |
| install.packages('SCCS', repos = 'https://cloud.r-project.org') |
- addat - Data on NSAID and antidepressant exposure and first GI bleed
- adidat - Data on antidiabetics and fractures
- amdat - Data on MMR and aseptic meningitis
- apdat - Data on antipsychotics and stroke
- autdat - Data on MMR vaccine and autism
- bpdat - Data on blood pressure and headaches
- bupdat - Data on bupropion and sudden death
- condat - Data on DTP and convulsions
- dtpdat - Data on DTP and convulsions
- febdat - Data on multitype convulsions and MMR
- gbsdat - Data on influenza vaccine and GBS
- gidat - Data on NSAID and first GI bleed
- hibdat - Data on DTP, Hib and convulsions
- hipdat - Data on antidepressants and hip fracture
- intdat - Data on intussusception and OPV
- itpdat - Data on MMR and ITP
- midat - Data on respiratory tract infections and myocardial infarction
- nrtdat - Data on NRT and MI
- opvdat - Data on OPV and intussusception
- pmdat - Data on asthma admissions and air pollution
- rotdat - Data on Rotavirus vaccine and intussusception
- rsvdat - Data on RSV and ambient temperature
- siddat - Data on hexavalent vaccine and sudden infant death syndrome
Conda:r-sccs-1.7(2025-03-25)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:bd6c6d4362. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 28 2025 |
| R-4.5-linux | OK | Mar 28 2025 |
| R-4.4-linux | OK | Mar 28 2025 |
Exports:eventdepenexpeventdepenobsformatdataintegrateIsplinelrtsccsnonparasccsquantsccssamplesizesemisccssimulatesccsdatasmoothagesccssmoothexposccsstandardsccs
Dependencies:ashbitopscliclustercolorspacecorpcordeSolvefansifarverfdafdsFNNggplot2gluegnmgtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmennetpcaPPpillarpkgconfigpracmaqvcalcR.methodsS3R6rainbowRColorBrewerRcppRCurlrelimprlangscalessurvivaltibbleutf8vctrsviridisLitewithr
Citation
To cite package ‘SCCS’ in publications use:
Weldeselassie YG, Whitaker H, Farrington P (2024). SCCS: The Self-Controlled Case Series Method. R package version 1.7, https://CRAN.R-project.org/package=SCCS.
ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.
Corresponding BibTeX entry:
@Manual{,
title = {SCCS: The Self-Controlled Case Series Method},
author = {Yonas Ghebremichael Weldeselassie and Heather Whitaker
and Paddy Farrington},
year = {2024},
note = {R package version 1.7},
url = {https://CRAN.R-project.org/package=SCCS},
}