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.