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mopa (version 1.0.1)

Species Distribution MOdeling with Pseudo-Absences

Description

Tools for transferable species distribution modeling and pseudo-absence data generation allowing the straightforward design of relatively complex experiments with multiple factors affecting the uncertainty (variability) of SDM outputs (pseudo-absence sample, climate projection, modeling algorithm, etc.), and the quantification of the contribution of different factors to the final variability following the method described in Deque el al. (2010) . Multiple methods for pseudo-absence data generation can be applied, including the novel Three-step method as described in Iturbide et al. (2015) . Additionally, a function for niche overlap calculation is provided, considering the metrics described in Warren et al. (2008) <10.1111/j.1558-5646.2008.00482.x> and in Pianka (1973) <10.1146/annurev.es.04.110173.000413>.

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Install

install.packages('mopa')

Monthly Downloads

19

Version

1.0.1

License

GPL-3

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Maintainer

Maialen Iturbide

Last Published

February 13th, 2018

Functions in mopa (1.0.1)

TSS.Stat

Internarl function for cutTSS
depthnames1

Depth names in a list 1
bindPresAbs

Bind presences and absences
Oak_phylo2

Oak distribution
depthnames

Depth length in a list
depthLength

Depth length in a list
biomat

Matrix with variables for modelling
modelo

Species distribution modeling and cross validation
Q_pubescens

Quercus pubsencens distribution
backgroundGrid

Create background coordinates from raster object
mods

Fitted models
backgroundRadius

Background extent restriction for a sequence of distances
extractFromModel

delimit

Delimit study area and background coordinates
mopaPredict

Model prediction
depth

Level depth in a list
kfold

Stratified random partitioning into subsets
leaveOneOut

Leave out a different subset for test each fold
boundingCoords

Bounding box coordinates of xy records
cutTSS

Cut value of the max TSS
varianceAnalysis

Variance analysis of RasterStack objects
mopaPredict0

Internal function for model prediction
nicheOver

Niche overlap
pseudoAbsences0

Pseudo-absences internal
pseudoAbsences

Pseudo-absence data generation
varianceSummary

Summary of the variance analysis results.
wrld

World map
extractFromModel0

extractFromPrediction

Extract values from objects or list of objects
mopaTrain

Easy species distribution modeling and cross validation
mopaTrain0

Easy species distribution modeling and cross validation
add_legend

AUCextentFit

Non-linear model fitting for extracting an index of the minimum value in x that obtains a value in y above the asymptote
OCSVMprofiling

Environmental profiling with One-Classification Support Vector Machine