Package: BLOQ 0.1-1
BLOQ: Impute and Analyze Data with BLOQ Observations
It includes estimating the area under the concentrations versus time curve (AUC) and its standard error for data with Below the Limit of Quantification (BLOQ) observations. Two approaches are implemented: direct estimation using censored maximum likelihood, also by first imputing the BLOQ's using various methods, then compute AUC and its standard error using imputed data. Technical details can found in Barnett, Helen Yvette, Helena Geys, Tom Jacobs, and Thomas Jaki. "Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification." Statistics in Biopharmaceutical Research (2020): 1-12. (available online: <https://www.tandfonline.com/doi/full/10.1080/19466315.2019.1701546>).
Authors:
BLOQ_0.1-1.tar.gz
BLOQ_0.1-1.zip(r-4.5)BLOQ_0.1-1.zip(r-4.4)BLOQ_0.1-1.zip(r-4.3)
BLOQ_0.1-1.tgz(r-4.4-any)BLOQ_0.1-1.tgz(r-4.3-any)
BLOQ_0.1-1.tar.gz(r-4.5-noble)BLOQ_0.1-1.tar.gz(r-4.4-noble)
BLOQ_0.1-1.tgz(r-4.4-emscripten)BLOQ_0.1-1.tgz(r-4.3-emscripten)
BLOQ.pdf |BLOQ.html✨
BLOQ/json (API)
# Install 'BLOQ' in R: |
install.packages('BLOQ', repos = c('https://vahidnassiri.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:bacaf5984d. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 18 2024 |
R-4.5-win | OK | Nov 18 2024 |
R-4.5-linux | OK | Nov 18 2024 |
R-4.4-win | OK | Nov 18 2024 |
R-4.4-mac | OK | Nov 18 2024 |
R-4.3-win | OK | Nov 18 2024 |
R-4.3-mac | OK | Nov 18 2024 |
Exports:estimateAUCandStdErrestimateAUCwithCMLperTimePointestimateAUCwithFullCMLestimateAUCwithMVNCMLestimateAUCwithPairwiseCMLimputeBLOQimputeCMLimputeConstantimputeKernelDensityEstimationimputeROSsimulateBealModelFixedEffectssimulateBealModelMixedEffects
Dependencies:digestgenericslatticemaxLikmiscToolsmvtnormsandwichzoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Estimate AUC and its standard error | estimateAUCandStdErr |
estimate AUC with censored maximum likelihood per time point | estimateAUCwithCMLperTimePoint |
estimate AUC with Full censored maximum likelihood | estimateAUCwithFullCML |
estimate AUC with multivariate normal censored maximum likelihood | estimateAUCwithMVNCML |
estimate AUCwith pairwise censored maximum likelihood | estimateAUCwithPairwiseCML |
impute BLOQ's with various methods | imputeBLOQ |
imputing BLOQ's using censored maximum likelihood | imputeCML |
imputing BLOQ's with a constant value | imputeConstant |
imputing BLOQ's using kernel density estimation | imputeKernelDensityEstimation |
imputing BLOQ's using regression on order statistics | imputeROS |
simulate data from Beal model with fixed effects | simulateBealModelFixedEffects |
simulate data from Beal model with fixed and random effects | simulateBealModelMixedEffects |