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inlabru (version 2.1.3)

Spatial Inference using Integrated Nested Laplace Approximation

Description

Facilitates spatial modeling using integrated nested Laplace approximation via the INLA package (). Additionally, implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. See Yuan Yuan, Fabian E. Bachl, Finn Lindgren, David L. Borchers, Janine B. Illian, Stephen T. Buckland, Havard Rue, Tim Gerrodette (2017), .

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Version

Install

install.packages('inlabru')

Monthly Downloads

1,492

Version

2.1.3

License

GPL (>= 2)

Homepage

http://www.inlabru.org

Maintainer

Fabian Bachl

Last Published

February 11th, 2018

Functions in inlabru (2.1.3)

gg.SpatialPixelsDataFrame

Geom for SpatialPixelsDataFrame objects

ggplot2 geomes for inlabru related objects

Render Spatial* and inla.mesh objects using RGL

Geom for predictions

glplot.inla.mesh

Visualize SpatialPoints using RGL

Pan-tropical spotted dolphins in the Gulf of Mexico.

Global setting for tutorial sessions

Convert a plot sample of points into one of counts.

Prediction from fitted bru model

Log Gaussian Cox process (LGCP) inference using INLA

glplot.SpatialPoints

Visualize SpatialPoints using RGL

glplot.SpatialLines

Visualize SpatialLines using RGL

Prediction from fitted inla model

Likelihood construction for usage with bru

Seal pup edata import

1-Dimensional NonHomogeneous Poisson example.

materncov.bands

Matern correlation or covariance function approximate credible bands.

refine.inla.mesh

Refine an inla.mesh object

import.mexdolphin

Mexdolphin data import

MRSea data import

Plot a map using extend of a spatial object

Marine renewables strategic environmental assessment

Plot a globe using rgl

Multiple ggplots on a page.

Gorilla Nesting Sites.

Generate integration points

Query if a point is inside the mesh boundary

gg.SpatialPolygons

Geom for SpatialPolygons objects

Simulated 1D animal group locations and group sizes

Geom for data.frame

import.gorillas

Gorilla data import

gg.SpatialPoints

Geom for SpatialPoints objects

plot.prediction

Plot prediction using ggplot2

Create a plot sample.

vertices.inla.mesh

Extract vertex locations from an inla.mesh

tsplit.inla.mesh

Split triangles of a mesh into four triangles

Posteriors of SPDE hyper parameters and Matern correlation or covariance function.

Sample from an inhomogeneous Poisson process

gg.inla.mesh.1d

Geom for inla.mesh.1d objects

Weighted summation (integration) of data frame subsets

Convert a data.frame of boundary points into a SpatialPolgonsDataFrame

Plot method for posterior marginals estimated by bru

Generate SpatialPixels covering an inla.mesh

Convert SpatialPoints and boundary polygon to spatstat ppp object

Convert data frame to SpatialLinesDataFrame

Coordinate transformation for spatial objects

Summary for a bru fit

Summarise DIC and WAIC from lgcp objects.

devel.cvmeasure

Variance and correlations measures for prediction components

Generate samples from fitted bru and inla models

Additional bru options

Convenient model fitting using (iterated) INLA

1-Dimensional NonHomogeneous Poisson example.

1-Dimensional Homogeneous Poisson example.

1D LGCP bin count simulation and comparison with data

gg.SpatialPixels

Geom for SpatialPixels objects

gg.SpatialGridDataFrame

Geom for SpatialGridDataFrame objects

gg.SpatialLines

Geom for SpatialLines objects

Geom for inla.mesh objects

Cross product of integration points

Geom for RasterLayer objects

Sampling based on bru posteriors

ggplot geom for spatial data

Geom for matrix