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ConfIntVariance  

Confidence Interval for the Univariate Population Variance without Normality Assumption
View on CRAN: Click here


Download and install ConfIntVariance package within the R console
Install from CRAN:
install.packages("ConfIntVariance")

Install from Github:
library("remotes")
install_github("cran/ConfIntVariance")

Install by package version:
library("remotes")
install_version("ConfIntVariance", "1.0.2")



Attach the package and use:
library("ConfIntVariance")
Maintained by
Mathias Fuchs
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-03-10
Latest Update: 2019-03-10
Description:
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) and the references therein for an overview of unbiased estimation of variances of U-statistics.
How to cite:
Mathias Fuchs (2019). ConfIntVariance: Confidence Interval for the Univariate Population Variance without Normality Assumption. R package version 1.0.2, https://cran.r-project.org/web/packages/ConfIntVariance. Accessed 15 Jul. 2026.
Previous versions and publish date:
1.0.2 (2019-03-10 11:02), (2026-07-09 08:00)
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Complete documentation for ConfIntVariance
Functions, R codes and Examples using the ConfIntVariance R package
Some associated functions: confIntVariance-package . varwci . 
Some associated R codes: main.R .  Full ConfIntVariance package functions and examples
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