The artemis
package was created to aid in the design and analysis of
eDNA survey studies by offering a custom suite of models for eDNA
sampling and qPCR data. It implements a set of Bayesian
latent-variable, truncated data models which are fit using
Stan.
The easiest way to install artemis
is from CRAN with the install.packages
function:
```{r, eval=FALSE, include=TRUE}
install.packages("artemis")
<br>
### Testing your installation
If your installation of `artemis` and its dependencies was successful, the following code should run without error (although you may see warning messages from `rstan` about Bulk/Tail Effective Samples Sizes being too low). If the first or second model returns an error that seems to have something to do with your `c++` compiler, you may need to [follow instructions to edit your `Makevars` or `Makevars.win` file](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started).
```{r, eval=FALSE}
library(artemis)
model_fit = eDNA_lm(Cq ~ scale(Distance_m) + scale(Volume_mL),
data = eDNA_data,
std_curve_alpha = 21.2, std_curve_beta = -1.5)
model_fit2 = eDNA_lmer(Cq ~ scale(Distance_m) + scale(Volume_mL) + (1|FilterID),
eDNA_data,
std_curve_alpha = 21.2, std_curve_beta = -1.5)
artemis
from sourceInstalling artemis
from source on Windows is not currently well-supported; we recommend installing from the pre-compiled binary if you're on Windows. If you're on MacOS or Linux and you prefer to install from source, then go ahead and do that with your function/utility of choice (devtools::install_github()
, utils::install.packages(type = "source")
, R CMD INSTALL
, etc.).
If you have sub-architecture you're really in to customizing, the source code is here, go nuts.
Please refer to the Getting Started with the artemis
package vignette, which covers most of the functionality of artemis.
Additional vignettes are forthcoming!
Please report all bugs via an issue at the package repo.
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