icensmis: Study Design and Data Analysis in the Presence of Error-Prone Diagnostic Tests and Self-Reported Outcomes
Version 1.3.1

We consider studies in which information from error-prone diagnostic tests or self-reports are gathered sequentially to determine the occurrence of a silent event. Using a likelihood-based approach incorporating the proportional hazards assumption, we provide functions to estimate the survival distribution and covariate effects. We also provide functions for power and sample size calculations for this setting.

AuthorXiangdong Gu and Raji Balasubramanian
Date of publication2016-01-03 17:44:50
MaintainerXiangdong Gu <ustcgxd@gmail.com>
LicenseGPL (>= 2)
Version1.3.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("icensmis")

Getting started

README.md

Popular man pages

datasim: Simulate data including multiple outcomes from error-prone...
icmis: Maximum likelihood estimation for settings of error-prone...
icpower: Study design in the presence of error-prone diagnostic tests...
icpowerpf: Study design in the presence of interval censored outcomes...
icpower.val: Study design in the presence of error-prone diagnostic tests...
See all...

All man pages Function index File listing

Man pages

datasim: Simulate data including multiple outcomes from error-prone...
icmis: Maximum likelihood estimation for settings of error-prone...
icpower: Study design in the presence of error-prone diagnostic tests...
icpowerpf: Study design in the presence of interval censored outcomes...
icpower.val: Study design in the presence of error-prone diagnostic tests...

Functions

Xmat_decode Source code
Xmat_norm Source code
bayesfit Source code
bayesmc Source code
bayesmc_pw Source code
bayesmc_pw_raw Source code
bayesmc_raw Source code
datasim Man page Source code
dmat Source code
fitsurv Source code
fitsurv_pw Source code
gamma_mean Source code
getrids Source code
gradlikA Source code
gradlikA0 Source code
gradlikB Source code
gradlikB0 Source code
gradlikC Source code
gradlikC0 Source code
gradlikTA Source code
gradlikTB Source code
gradlik_lamb Source code
gradlik_pw Source code
iclasso Source code
iclasso_pw Source code
iclasso_pw_raw Source code
iclasso_raw Source code
icmis Man page Source code
icpower Man page Source code
icpower.val Man page Source code
icpowerpf Man page Source code
lassofit Source code
lassofit_pw Source code
lassofit_pw_raw Source code
lassofit_raw Source code
loglikA Source code
loglikA0 Source code
loglikB Source code
loglikB0 Source code
loglikC Source code
loglikC0 Source code
loglikTA Source code
loglikTB Source code
loglik_lamb Source code
loglik_pw Source code
loglik_pw_raw Source code
loglik_raw Source code
maxlambda Source code
maxlambda_pw Source code
maxlambda_pw_raw Source code
maxlambda_raw Source code
powerdmat1 Source code
powerdmat2 Source code
powerdmat3 Source code
powerdmat4 Source code
simoutcome Source code
timeMat Source code

Files

tests
tests/testthat.R
tests/testthat
tests/testthat/test_power.R
src
src/loglikC.cpp
src/dataproc.cpp
src/loglikA.cpp
src/powerfuncs.cpp
src/HighDimCR.cpp
src/loglikB.cpp
src/RcppExports.cpp
NAMESPACE
R
R/HighDimCR.R
R/icpower.R
R/icmis.R
R/datasim.R
R/RcppExports.R
R/icpower.val.R
R/icpowerpf.R
README.md
MD5
DESCRIPTION
man
man/icmis.Rd
man/datasim.Rd
man/icpower.Rd
man/icpowerpf.Rd
man/icpower.val.Rd
icensmis documentation built on May 19, 2017, 8:50 p.m.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.