The wiqid package

Currently it has functions for estimating occupancy, abundance from closed captures, density from spatial capture-recaptures, and survival from mark-recapture data, plus a slew of functions for species richness and alpha and beta diversity.

It is intended to be used for (1) simulations and bootstraps, (2) teaching, and (3) introducing Bayesian methods. And it should work on all platforms: Windows, Linux, and Mac.

It is available from the CRAN repository: do install.packages("wiqid") in the R console. The code for the latest development version is on GitHub. The main help page (do ?wiqid) has a list of the functions available and each has a comprehensive help page with examples.

Simulations and bootstraps

wiqid started life as a collection of Quick functions with no input checks (hence Dirty) for use in long runs of simulations in minimum time. One upshot of this requirement is the range of functions for each type of analysis, with simpler and faster functions available for the simpler models, while still producing identical results. Functions do not throw an error if the analysis fails (eg, a mark-recapture data set with no recaptures), but returns NA. We have used wiqid for simulations workshops since 2013.

Teaching

We use wiqid for analysis of example data sets in our basic stats workshops: binary, count and continuous data, and occupancy and mark-recapture data. Input checks

In this context, Quick means minimum user time rather than run time, in particular a user interface which is consistent with the standard functions lm and glm and with secr.fit in the secr package (Efford, 2013), all of which we use in basic workshops. We use R's formula notation (y ~ x1 + x2 +...) to specify models and straight-forward data frames for data.

Before 2013, we used PRESENCE, DISTANCE, MARK and EstimateS for these analyses. Using wiqid instead gives us more time to discuss basic concepts, the assumptions of the analysis, and appropriate study designs.

Bayesian methods

Our basic workshops give priority to Bayesian methods, and wiqid has functions for Bayesian analysis with similar syntax to the MLE functions, with the addition of arguments to define prior distributions. Many of these use R code to generate MCMC chains, some use JAGS software, but users of wiqid do not need to write JAG code. In all cases the output is returned as an object of class mcmcOutput from the mcmcOutput package, which has functions for summarising and plotting posterior distributions. (Note that this now replaces the Bwiqid class used in wiqid versions prior to 0.3.0.)

Acknowledgements

Back in 2007 I looked at the source code for WiSP (Zucchini et al 2007); I learnt a lot about coding for maximum likelihood estimation and contributed some patches. Murray Efford's secr package (Efford 2013) was the inspiration for the user interface and syntax, in particular the use of a list of double-sided formulae to define models. My collaboration with John Kruschke on the BEST package (Kruschke & Meredith 2013) opened the way for Quick and Dirty Bayesian functions for the main wildlife-related models.

The development of wiqid has been driven by discussions with the participants and instructors on more than 30 workshops run by BCSS over the years.

References

Efford, M. G. (2013). secr: Spatially explicit capture-recapture models. https://CRAN.R-project.org/package=secr

John K. Kruschke and Mike Meredith (2013). BEST: Bayesian Estimation Supersedes the t-Test. https://CRAN.R-project.org/package=BEST

Zucchini, W., Borchers, D.L., Erdelmeier, M., Rexstad, E. and Bishop, J. 2007. WiSP 1.2.4. Institut fur Statistik und Okonometrie, Georg-August-Universitat Gottingen, Platz der Gottinger Seiben 5, Gottingen, Germany.