Confidence Interval for the Univariate Population Variance
without Normality Assumption
Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.

Package to Parse an INI File, Including Variable Interpolation
person("Holger", "Hoefling", email = "hhoeflin@gmail.com", role = c("aut", "cre"))
Enhances the 'ini' package by adding the ability to interpolate variables.
The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module)
and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables.

person(given = "Yachong",
family = "Yang",
role = c("aut", "cre"),
email = "yachong@wharton.upenn.edu")
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>.
glmnet, mvtnorm, stats, MASS, quantregForest
testthat (>= 3.0.0), knitr, rmarkdown, ggplot2, repr

Sensitivity Analyses for Unmeasured Confounding in Meta-Analyses
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.