Title: Efficient Tuning-Free Conformal Prediction
Description: An implementation of efficiency first conformal prediction (EFCP) and validity first conformal prediction (VFCP) that demonstrates both validity (coverage guarantee) and efficiency (width guarantee). To learn how to use it, check the vignettes for a quick tutorial. The package is based on the work by Yang Y., Kuchibhotla A.,(2021) <arxiv:2104.13871>.
Imports: glmnet, mvtnorm, stats, MASS, quantregForest
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, ggplot2, repr

Package: ConfoundedMeta
Type: Package
Title: Sensitivity Analyses for Unmeasured Confounding in Meta-Analyses
Version: 1.0.0
Date: 2017-05-31
Author: Maya B. Mathur, Tyler J. VanderWeele
Maintainer: Maya B. Mathur <maya.z.mathur@gmail.com>
Description: Conducts sensitivity analyses for unmeasured confounding in
random-effects meta-analysis per Mathur & VanderWeele (in preparation).
Given output from a random-effects meta-analysis with a relative risk
outcome, computes point estimates and inference for: (1) the proportion
of studies with true causal effect sizes more extreme than a specified threshold
of scientific significance; and (2) the minimum bias factor and confounding
strength required to reduce to less than a specified threshold the proportion
of studies with true effect sizes of scientifically significant size.
Creates plots and tables for visualizing these metrics across a range of bias values.
License: GPL-2
Imports: ggplot2 (>= 2.2.1), metafor, stats
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-06-01 22:57:12 UTC; mmathur
Repository: CRAN
Date/Publication: 2017-06-02 17:25:03 UTC

Package: ConfoundedMeta
Type: Package
Title: Sensitivity Analyses for Unmeasured Confounding in Meta-Analyses
Version: 1.1.0
Date: 2017-05-31
Author: Maya B. Mathur, Tyler J. VanderWeele
Maintainer: Maya B. Mathur <maya.z.mathur@gmail.com>
Description: Conducts sensitivity analyses for unmeasured confounding in
random-effects meta-analysis per Mathur & VanderWeele (in preparation).
Given output from a random-effects meta-analysis with a relative risk
outcome, computes point estimates and inference for: (1) the proportion
of studies with true causal effect sizes more extreme than a specified threshold
of scientific significance; and (2) the minimum bias factor and confounding
strength required to reduce to less than a specified threshold the proportion
of studies with true effect sizes of scientifically significant size.
Creates plots and tables for visualizing these metrics across a range of bias values.
Provides tools to easily scrape study-level data from a published forest plot or
summary table to obtain the needed estimates when these are not reported.
License: GPL-2
Imports: ggplot2 (>= 2.2.1), metafor, stats
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-06-12 12:47:34 UTC; mmathur
Repository: CRAN
Date/Publication: 2017-06-12 16:01:16 UTC