Software

xsdm

xsdm: climate variability and species ranges

Species distribution models almost always treat climate as an average. Our new modelling framework shows climatic variability constrains species geographic ranges as much as mean climate does. The associated R package provides extensive inference tools supporing practical application of the new approach.

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cheddar

cheddar: food-web visualization software

R package for analysis and visualisation of food-web data developed by former Reuman lab PhD student Lawrence Hudson while he was in the lab. Downloaded >90K times, cited by >100 scientific papers.

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tsvr

tsvr: timescale-specific variance ratios for community stability

R package containing tools for timescale decomposition of the classic variance ratio of community ecology.

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wsyn

wsyn: wavelet methods for spatial synchrony

R package for implementing wavelet tools for analyzing spatial synchrony, including the wavelet Moran theorem, synchrony attribution theorem, and wavelet linear modelling tools developed in the Reuman lab.

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ucminfcpp

ucminfcpp: a C++ implementation of the ucminf optimizer

R package by Reuman lab postdoc Angel Robles Fernandez. A C++ port of the optimization algorithm in the R package ucminf, built to interoperate faster and more cleanly with xsdm (but has other applications, of course).

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sobol

sobol: a fast implementation of Sobol sequences

R package by Reuman lab postdoc Angel Robles Fernandez. A fast implementation of Sobol low-discrepancy sequences, built to interoperate cleanly with xsdm for speed (but has other applications, of course).

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maxentcpp

maxentcpp: a C++ implementation of MaxEnt for species distribution modeling

R package by Reuman lab postdoc Angel Robles Fernandez. A modern C++ reimplementation of the MaxEnt species-distribution-modeling algorithm (originally a Java tool).

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pomp

pomp: inference for partially observed Markov processes

An R package by Aaron King (University of Michigan) and collaborators for simulation- and likelihood-based inference on partially observed Markov process models. Widely used in ecology and epidemiology. Dan contributed in a minor way, but linking here because it is cool!

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