Package: augSIMEX 3.7.4

Qihuang Zhang

augSIMEX: Analysis of Data with Mixed Measurement Error and Misclassification in Covariates

Implementation of the augmented Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777> for analyzing the data with mixed measurement error and misclassification. The main function provides a similar summary output as that of glm() function. Both parametric and empirical SIMEX are considered in the package.

Authors:Qihuang Zhang <[email protected]>, Grace Y. Yi <[email protected]>

augSIMEX_3.7.4.tar.gz
augSIMEX_3.7.4.tar.gz(r-4.5-noble)augSIMEX_3.7.4.tar.gz(r-4.4-noble)
augSIMEX_3.7.4.tgz(r-4.4-emscripten)augSIMEX_3.7.4.tgz(r-4.3-emscripten)
augSIMEX.pdf |augSIMEX.html✨
augSIMEX/json (API)

# Install 'augSIMEX' in R:
install.packages('augSIMEX', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • GeneRepeat - Example data for univariate error-prone covariates in repeated measurements case
  • GeneUni - Example of genetic data for univariate error-prone covariates
  • ToyMult - Toy example data for multivariate error-prone covariates
  • ToyRepeat - Toy example data for univariate error-prone covariates in repeated measurements case
  • ToyUni - Toy example data for univariate error-prone covariates

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 10 scripts 258 downloads 9 exports 4 dependencies

Last updated 5 years agofrom:e990a81bdb. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 14 2024
R-4.5-linux-x86_64OKDec 14 2024

Exports:augSIMEXcoef.augSIMEXglmscorelogLik.augSIMEXplot.augSIMEXpredict.augSIMEXresiduals.augSIMEXsummary.augSIMEXvcov.augSIMEX

Dependencies:FormulaMASSnleqslvRcpp