Package: VFP 1.4.1
Andre Schuetzenmeister
VFP: Variance Function Program
Variance function estimation for models proposed by W. Sadler in his variance function program ('VFP', <http://www.aacb.asn.au/resources/useful-tools/variance-function-program-v14>). Here, the idea is to fit multiple variance functions to a data set and consequently assess which function reflects the relationship 'Var ~ Mean' best. For 'in-vitro diagnostic' ('IVD') assays modeling this relationship is of great importance when individual test-results are used for defining follow-up treatment of patients.
Authors:
VFP_1.4.1.tar.gz
VFP_1.4.1.tar.gz(r-4.5-noble)VFP_1.4.1.tar.gz(r-4.4-noble)
VFP_1.4.1.tgz(r-4.4-emscripten)VFP_1.4.1.tgz(r-4.3-emscripten)
VFP.pdf |VFP.html✨
VFP/json (API)
# Install 'VFP' in R: |
install.packages('VFP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- B2mIntra_98 - Example Data B2mIntra_98.VFP (Beta-2-microglobulin RIA) from the Variance Function Program 12.0 from Sadler
- Glucose - Example Data Glucose.VFP from the Variance Function Program 12.0 from Sadler
- MultiLotReproResults - Result of a Real-World Precsion Experiment on 10 Samples
- RealData1 - Result of a Real-World Precsion Experiment on 10 Samples
- ReproData - Serum Work Area Example Data of a Reproducibility Experiment
- T4S9_99 - Example Data T4S9_99.VFP (T4 RIA) from the Variance Function Program 12.0 from Sadler
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:da114228bf. Checks:OK: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 04 2024 |
R-4.5-linux | NOTE | Dec 04 2024 |
Exports:as.rgbcoef.VFPconditionHandlerderiveCxfit.vfpgetMat.VCAlegend.rmplot.VFPpowfun2simplepowfun3powfun3simplepowfun4powfun4simplepowfun5powfun5simplepowfun6powfun6simplepowfun7powfun7simplepowfun8powfun8simplepowfun9simpleprecisionPlotpredict.VFPpredictMeanprint.VFPsummary.VFP
Dependencies:bootgnmlatticelme4MASSMatrixminqanlmenloptrnnetnumDerivqvcalcRcppRcppEigenrelimpVCA