Installing the wiqid package

There is an introduction to wiqid here . The package is still getting major changes, meaning that your old scripts may not work with new versions of wiqid .

Some of the simpler Bayesian MCMC functions in wiqid are coded in R, others use JAGS and the rjags package.

Installing wiqid

wiqid is now on CRAN !

You can now install wiqid and the packages it requires from within R with the command install.packages("wiqid") . You can also go to the Packages > Install package(s)... menu item and select wiqid from the pop-up list, but with more than 11,000 packages on the list, I find typing easier.

After installing wiqid

For full functionality, you will need to have JAGS installed, as well as the rjags, secr and shiny packages.

1. JAGS: If you are using Mac or Ubuntu, check out the notes here or here . Go to the JAGS download page . A link at the top of the page gives you the latest installer; on Windows, this takes you to an HTML page with a choice of installers, depending on the version of R you are using. Install in the default directory.

JAGS 4 was released in October 2015; to use JAGS 4 you need version of rjags_4-4 or later.

2. rjags : Once you have installed JAGS, you can install the rjags package in R with

install.packages("rjags")

Check that it has been installed correctly and can link to JAGS by doing

library(rjags)

3. secr and shiny : You can install these in R with

install.packages(c("secr", "shiny"))

Both these packages depend on a number of other packages, and they will also be downloaded automatically.

Latest devel version

wiqid is hosted on the Github repository here . Changes will appear on Github before being made in the version released on CRAN. if you want to install the devel version, install the R package githubinstall then use:

githubinstall::githubinstall("wiqid")

As usual, devel versions should work properly but have not been fully tested: use with care!

The wiqid package for R offers a practical toolkit for wildlife researchers who need quick, reliable statistical analysis without excessive complexity. Built around the estimation of animal density, detection probability, and occupancy, it supports Bayesian methods through both its own functions and integration with JAGS and rjags. The package is particularly useful for capture-recapture studies, camera trap surveys, and spatial explicit capture-recapture (SECR) modeling. Its name—Quick and Dirty—reflects a philosophy of delivering functional results efficiently, making it suitable for field ecologists who need to process data between seasons or prepare preliminary estimates before deeper analysis. The package continues to evolve, with regular updates available through CRAN.

Bayesian estimation lies at the heart of many wiqid functions, allowing users to generate posterior distributions for key parameters like detection probability, density, and survival rates. The package includes Gibbs samplers and MCMC chains that help researchers produce confidence intervals and credible intervals for their wildlife estimates. This approach is especially valuable when working with sparse data from rare or elusive species, where traditional maximum likelihood methods may struggle. By providing simple interfaces to these powerful statistical tools, wiqid lowers the barrier for ecologists who want to incorporate Bayesian reasoning into their work without needing to write complex sampling algorithms from scratch. The resulting outputs integrate smoothly with R's broader ecosystem for further plotting and model comparison.

Occupancy modeling is another core capability within wiqid, enabling researchers to estimate the proportion of sites occupied by a species while accounting for imperfect detection. This is essential for understanding habitat use, species distribution, and population trends over time. The package handles single-season and multi-season models, allowing users to incorporate site covariates and detection covariates that might influence whether animals are present or simply missed during surveys. By producing estimates of occupancy probability alongside detection probability, wiqid helps wildlife managers make informed decisions about habitat conservation, species reintroduction, and monitoring program design. These tools have been applied to studies of mammals, birds, amphibians, and reptiles across diverse landscapes.

The package's spatial capabilities extend to home range analysis and activity pattern estimation, supporting research on how animals use space and time. Functions for analyzing camera trap data allow users to calculate overlap indices between species or between different time periods, revealing patterns of temporal partitioning and behavioral responses to environmental change. The inclusion of spatial explicit capture-recapture methods means researchers can estimate population density directly from spatially referenced detection data, accounting for individual movement and trap placement. These features make wiqid a versatile choice for landscape-scale studies where understanding animal space use is critical for conservation planning, road ecology assessments, and habitat connectivity analyses.

Installation and use of wiqid have been streamlined through its availability on CRAN, making it accessible to R users worldwide with a simple command. The package works across Windows, Mac, and Linux systems, and its dependencies are carefully managed to reduce compatibility issues. For those new to wildlife analysis, the package includes documentation and worked examples that demonstrate typical workflows from data import through model fitting to result interpretation. While the software undergoes major changes between versions—sometimes breaking older scripts—the development process prioritizes improved accuracy and expanded functionality. The broader community of wildlife statisticians and ecologists continues to contribute feedback, ensuring that wiqid remains responsive to the real-world needs of field research and conservation monitoring.