The estmeansd package implements the methods of McGrath et al. (2020) and Cai et al. (2021) for estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. Specifically, these methods can be applied to studies that report one of the following sets of summary statistics:

• S1: median, minimum and maximum values, and sample size
• S2: median, first and third quartiles, and sample size
• S3: median, minimum and maximum values, first and third quartiles, and sample size

Additionally, the Shiny app estmeansd implements these methods.

You can install the released version of estmeansd from CRAN with:

install.packages("estmeansd")

After installing the devtools package (i.e., calling install.packages(devtools)), the development version of estmeansd can be installed from GitHub with:

devtools::install_github("stmcg/estmeansd")

Specifically, this package implements the Box-Cox (BC), Quantile Estimation (QE), and Method for Unknown Non-Normal Distributions (MLN) approaches to estimate the sample mean and standard deviation. The BC, QE, and MLN methods can be applied using the bc.mean.sd() qe.mean.sd(), and mln.mean.sd() functions, respectively:

library(estmeansd)
set.seed(1)
bc.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100) # BC Method
#> $est.mean
#> [1] 4.210971
#> 
#> $est.sd
#> [1] 1.337348
qe.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100) # QE Method
#> $est.mean
#> [1] 4.347284
#> 
#> $est.sd
#> [1] 1.502171
mln.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100) # MLN Method
#> $est.mean
#> [1] 4.195238
#> 
#> $est.sd
#> [1] 1.294908