Articles by A.M. Barbosa

modEvA 2.0 now on CRAN!

January 21, 2020 | A.M. Barbosa

The new version of modEvA (2.0) is now on CRAN! It can produce some new plots, such as the precision-recall curve and the histograms/densities of predicted values for presence and absence observations. It calculates some additional evaluation measures, such as … Continue reading → [Read more...]

Area under the precision-recall curve

January 17, 2020 | A.M. Barbosa

The AUC function, in the modEvA package, initially computed only the area under the receiver operating characteristic (ROC) curve. Now, since modEvA version 1.7 (currently available on R-Forge), it also offers the option to compute the precision-recall curve, which may … Continue reading →
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Variable selection with multGLM

January 9, 2020 | A.M. Barbosa

The ‘multGLM‘ function in the ‘fuzzySim‘ R package automatically calculates generalized linear models for one or more species, with a range of options for variable selection. However, you may want to build your final models with other methods (e.g. multimodel-inference … Continue reading → [Read more...]

Plot shared favourability for two competing species

March 8, 2019 | A.M. Barbosa

The sharedFav function below implements the graphical analyses of Acevedo et al. (2010, 2012) on biogeographical interactions. It takes two vectors of favourability values at different localities for, respectively, a stronger and a weaker competing species (or two equally strong … Continue reading →
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Package fuzzySim now on CRAN!

December 19, 2018 | A.M. Barbosa

The fuzzySim package, which is regularly maintained on R-Forge, is finally also available on the CRAN official repository, in its latest version 2.0! Many thanks especially to Alba Estrada for invaluable help with bug search. [Read more...]

Improvements to fuzzySim functions

November 21, 2018 | A.M. Barbosa

A few improvements were recently made to several functions in the fuzzySim package. Mainly, function modelTrim is now more independent (it used to require “attach” sometimes); multTSA allows either “AIC” or “significance” as a backward stepwise selection criterion, and provides … Continue reading → [Read more...]
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