Edlin Guerra-Castro, Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro

**SSP** is an R package design to estimate sampling effort in studies of ecological communities based on the definition of *pseudo*-multivariate standard error (*MultSE*) (Anderson & Santana-Garcon 2015), simulation of data and resampling (Guerra-Castro et al., 2020).

**SSP** includes seven functions: `assempar`

for extrapolation of assemblage parameters using pilot data; `simdata`

for simulation of several data sets based on extrapolated parameters; `datquality`

for evaluation of plausibility of simulated data; `sampsd`

for repeated estimations of *MultSE* for different sampling designs in simulated data sets; `summary_sd`

for summarizing the behavior of *MultSE* for each sampling design across all simulated data sets, `ioptimum`

for identification of the optimal sampling effort, and `plot_ssp`

to plot sampling effort vs *MultSE*.

- Required: vegan, sampling, stats, ggplot2. These are installed automatically.
- Suggested: devtools, knitr, and rmarkdown to build
**SSP**from github. All these must be installed by you.

The **SSP** package will be available on CRAN but can be downloaded from github using the following commands:

```
## Packages needed to build SSP and vignettes
install.packages(pkgs = c('devtools', 'knitr', 'rmarkdown'))
library(devtools)
library(knitr)
library(rmarkdown)
## install the latest version of SSP from github
install_github('edlinguerra/SSP', build_vignettes = TRUE)
library(SSP)
```

For examples about how to use **SSP**, see `help('SSP')`

after instalation.