geocode
(Package: dismo) :
Georeferencing with Google
A wrapper around the Google geocoding web-service. It returns 0 to n matches. It is important to be as precise as possible, e.g. always include the country in the locality description.
The function takes an inital cross-validated model as produced by gbm.step and then assesses the potential to remove predictors using k-fold cross validation. This done for each fold, removing the lowest contributing predictor, and repeating this process for a set number of steps. After the removal of each predictor, the change in predictive deviance is computed relative to that obtained when using all predictors. The function returns a list containing the mean change in deviance and its standard error as a function of the number of variables removed. Having completed the cross validation, it then identifies the sequence of variable to remove when using the full data set, testing this up to the number of steps used in the cross-validation phase of the analysis with results reported to the screen.
The Bioclim algorithm has been extensively used for species distribution modeling. Bioclim is the classic 'climate-envelope-model'. Although it generally does not perform as good as some other modeling methods (Elith et al. 2006) and is unsuited for predicting climate change effects (Hijmans and Graham, 2006). It is still used, however, among other reasons because the algorithm is easy to understand and thus useful in teaching species distribution modeling.
k-fold partitioning of a data set for model testing purposes. Each record in a matrix (or similar data structure) is randomly assigned to a group. Group numbers are between 1 and k.
This function downloads species occurence records from the Global Biodiversity Information Facility (GBIF) data portal. You can download either a single species (if you append a '*' to the species name) or a subspecies of comparable level. You can download the data for an entire genus by using species='*'. Before using this function, please first check the GBIF data use agreement.
The geographic distance model predicts that the likelyhood of presence is highest near places where a species has been observed. It can be used as a null-model to calibrate cross-validation scores with.
providing 
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