During my modelling antics these last couple of days I seem to have been using many of the add-ins I’ve have installed with the software I use regularly. I thought I’d highlight some of them here as they are really useful tools that can expand the modelling and data manipulation possibilities of a standard software install.

Much of the modelling I do is spatial, so I’m regularly using some form of GIS. I’m most familiar with the ESRI products, but have also tinkered with things like GRASS. Two free add-ins that are really useful if you use ArcMap regularly are the Patch Analyst and Hawth’s Tools. Patch Analyst facilitates the spatial pattern analysis (making use of FRAGSTATS) of landscape patches, and the modelling of attributes associated with patches. Hawth’s Tools is an extension for ArcMap that performs a number of spatial analyses and functions that you can’t do with the standard install of ArcMap. Most of the tools are written with ecological analyses in mind, but it’s also be useful for non-ecologists with functions such as conditional point sampling, kernel density estimation and vector layer editing.

Although it is generally frowned upon for statistics (use R – see below), Microsoft Excel isn’t a bad tool for organising small and medium-sized data sets and for doing basic systems modelling (spatial simulation is a little trickier). Developed by some guys at CSIRO, Pop Tools is a free add-in for PC versions of Excel that facilitates analysis of matrix population models and the simulation of stochastic processes. It was originally written to analyse ecological models, but has been used for studies of population dynamics, financial modelling, and the calculation of bootstrap and resampling statistics. Once installed, PopTools puts a new menu item in Excel’s main menu and adds over a hundred useful worksheet functions. Regardless of whether you intend to do any modelling in Excel or not, the ASAP Utilities add-in is a must for automating many frequently required tasks (including those you didn’t even know you wanted to do in Excel!). There are selection tools (such as ‘select cell with smallest number’), text tools (such as ‘insert before current value’), information tools (such as ‘Find bad cell references (#REF!)’) and many more.

If you’re going to be doing any serious statistical analyses the current software of choice is R, the open-source language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques. If you need to analyse or manipulate large datasets R is for you – you are only restricted by the memory available on your computer. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. R is also highly extensible by installing optional packages that have been written by users from around the world.

Many of the packages I use are from the Spatial and Environmetrics Task views. For example, I use spdep for calculating spatial autocorrelation, VR for computing spatial correlograms or confidence intervals for model parameters, and hier.part for hierarchical partitioning. This week I started thinking about how I will use the yaImpute package to impute the stand vegetation data we have collected at specific points in our study area across the entire landscape ready to initialise our spatial simulation model. Download the R software and the individual packages from a CRAN mirror near you.

Of course, this is just the tip of the iceberg and only a few of the most useful add-ins for the most commonly used software. For a much more complete list of more technical software and programming tools for ecological and environmental modelling see Andrea Emilio Rizzoli’s ‘Collection of Modelling and Simulation Resources on the Internet‘ or the list of Ecological Modelling links by T. Legovic’ and J. Benz.