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:

`::install_github("stmcg/estmeansd") devtools`

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
```