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cvcqv provides some easy-to-use functions and classes to calculate Coefficient of Variation (cv) and Coefficient of Quartile Variation (cqv) with confidence intervals provided with all available methods.



There are abundant methods available for the calculation of confidence intervals of a dispersion measure like coefficient of variation (cv) or coefficient of quartile variation (cqv), which have not yet been implemented in R. Also, cqv is quite useful in conditions where the distribution of variables does not follow normal distribution.

Coefficient of Variation

cv is a measure of relative dispersion representing the degree of variability relative to the mean (Albatineh et al, 2014). Since cv is unitless, it is useful for comparison of variables with different units. It is also a measure of homogeneity (Albatineh et al, 2014).

Coefficient of Quartile Variation

cqv is a measure of relative dispersion that is based on interquartile range (IQR). Since cqv is unitless, it is also useful for comparison of variables with different units. It is also a measure of homogeneity (Bonett, 2006; Altunkaynak, 2018).

Confidence Interval

Since the measurements of cv and cqv are obtained from samples, we cannot easily generalize them and decide based upon them. Confidence Intervals (CI) help us to make a probabilistic interval around the estimation of calculated cv and cqv. For example, 95% CI indicates that it is 95% probable that the measurement for a population lies between the lower and upper bounds of that CI.


The authors’ intention is to create a small R package adhering to K-I-S-S principle to facilitate the use of the most available rigorous methods for calculation of confidence intervals around the variability measures of cv and cqv.


We are bound by the high standards of functional programming (FP) and object-oriented programming (OOP). The majority of tools provided by cvcqv are developed as both FP tools and R6 classes, for sake of versatility, portability and efficiency.

Getting started

If you are an ubuntu user, you are going to need these non-R packages:

sudo apt install libcurl4-openssl-dev libssl-dev libxml2-dev libgsl-dev   

The cvcqv is available on github. To install it in R, use:


* Currently, these tools are available:

name is.R6.. Description
CoefVar TRUE Coefficient of Variation (cv)
CoefQuartVar TRUE Coefficient of Quartile Variation (cqv)
CoefVarCI TRUE Confidence Intervals for cv
CoefQuartVarCI TRUE Confidence Intervals for cqv
SampleQuantiles TRUE Sample Quantiles
cv_versatile FALSE Coefficient of Variation
cqv_versatile FALSE Coefficient of Quartile Variation
BootCoefVar TRUE Bootstrap Resampling for cv
BootCoefQuartVar TRUE Bootstrap Resampling for cqv

* This package is inspired by dplyr, R6, SciView, boot, and MBESS.


Here, we want to observe all available confidence intervals for the cv of variable x:

x <- c(
    0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
    4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9
results <- CoefVarCI$new(x, digits = 3)$all_ci()  # R6 class
# or alternatively: 
results <- cv_versatile(x, digits = 3, method = "all")  # functional programming

The results will be:

est lower upper description
kelley 57.774 41.287 97.894 cv with Kelley 95% CI
mckay 57.774 41.441 108.483 cv with McKay 95% CI
miller 57.774 34.053 81.495 cv with Miller 95% CI
vangel 57.774 41.264 105.426 cv with Vangel 95% CI
mahmoudvand_hassani 57.774 43.476 82.857 cv with Mahmoudvand-Hassani 95% CI
equal_tailed 57.774 43.937 84.383 cv with Equal-Tailed 95% CI
shortest_length 57.774 42.015 81.013 cv with Shortest-Length 95% CI
normal_approximation 57.774 44.533 85.272 cv with Normal Approximation 95% CI
norm 57.774 38.799 78.937 cv with Normal Approximation Bootstrap 95% CI
basic 57.774 35.055 78.167 cv with Basic Bootstrap 95% CI
perc 57.774 38.879 79.174 cv with Bootstrap Percentile 95% CI
bca 57.774 40.807 82.297 cv with Adjusted Bootstrap Percentile (BCa) 95% CI

Next, we want to find all of the available confidence intervals for the cqv of variable x:

results <- CoefQuartVarCI$new(x, digits = 3)$all_ci()  # R6 class
# or alternatively:
results <- cqv_versatile(x, , digits = 3, method = "all")  # functional programming

The results will be:

est lower upper description
bonett 45.625 24.785 77.329 cqv with Bonett CI
norm 45.625 19.957 70.840 cqv with normal approximation CI
basic 45.625 18.992 73.917 cqv with basic bootstrap CI
percent 45.625 17.122 68.683 cqv with bootstrap percentile CI
bca 45.625 24.273 83.264 cqv with adjusted bootstrap percentile (BCa) CI


Download the cvcqv_1.0.0.tar.gz. Install the source package cvcqv from the Packages >> Install >> Package Archive File (.tar.gz) >> Browse >> cvcqv_1.0.0.tar.gz. Or run an installation code like:

install.packages("~/cvcqv_1.0.0.tar.gz", repos = NULL, type = "source")

Then, browse for vignettes:


Then, select html to show the vignette.


Albatineh, AN., Kibria, BM., Wilcox, ML., & Zogheib, B, 2014, Confidence interval estimation for the population coefficient of variation using ranked set sampling: A simulation study, Journal of Applied Statistics, 41(4), 733–751, DOI:

Bonett, DG., 2006, Confidence interval for a coefficient of quartile variation, Computational Statistics & Data Analysis, 50(11), 2953-7, DOI:

Altunkaynak, B., Gamgam, H., 2018, Bootstrap confidence intervals for the coefficient of quartile variation, Simulation and Computation, 1-9, DOI: