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inlabru (version 2.1.1)

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

2.1.1

License

GPL (>= 2)

Homepage

http://www.inlabru.org

Maintainer

Fabian Bachl

Last Published

November 10th, 2017

Functions in inlabru (2.1.1)

Cross product of integration points

Geom for RasterLayer objects

Geom for data.frame

gg.inla.mesh.1d

Geom for inla.mesh.1d objects

MRSea data import

ggplot2 geomes for inlabru related objects

Seal pup edata import

Multiple ggplots on a page.

Generate SpatialPixels covering an inla.mesh

Convert SpatialPoints and boundary polygon to spatstat ppp object

Additional bru options

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

gg.SpatialPolygons

Geom for SpatialPolygons objects

glplot.inla.mesh

Visualize SpatialPoints using RGL

ggplot geom for spatial data

import.gorillas

Gorilla data import

import.mexdolphin

Mexdolphin data import

Query if a point is inside the mesh boundary

Log Gaussian Cox process (LGCP) inference using INLA

Convert data frame to SpatialLinesDataFrame

Create a plot sample.

Convert a plot sample of points into one of counts.

tsplit.inla.mesh

Split triangles of a mesh into four triangles

Convert a data.frame of boundary points into a SpatialPolgonsDataFrame

Coordinate transformation for spatial objects

1-Dimensional Homogeneous Poisson example.

1-Dimensional NonHomogeneous Poisson example.

gg.SpatialPoints

Geom for SpatialPoints objects

Summarise DIC and WAIC from lgcp objects.

devel.cvmeasure

Variance and correlations measures for prediction components

gg.SpatialGridDataFrame

Geom for SpatialGridDataFrame objects

gg.SpatialLines

Geom for SpatialLines objects

Sampling based on bru posteriors

Geom for inla.mesh objects

Geom for predictions

Plot a map using extend of a spatial object

Gorilla Nesting Sites.

Pan-tropical spotted dolphins in the Gulf of Mexico.

Marine renewables strategic environmental assessment

Generate samples from fitted bru and inla models

glplot.SpatialLines

Visualize SpatialLines using RGL

glplot.SpatialPoints

Visualize SpatialPoints using RGL

Global setting for tutorial sessions

refine.inla.mesh

Refine an inla.mesh object

Sample from an inhomogeneous Poisson process

Summary for a bru fit

Simulated 1D animal group locations and group sizes

1-Dimensional NonHomogeneous Poisson example.

1D LGCP bin count simulation and comparison with data

Convenient model fitting using (iterated) INLA

gg.SpatialPixels

Geom for SpatialPixels objects

gg.SpatialPixelsDataFrame

Geom for SpatialPixelsDataFrame objects

Plot method for posterior marginals estimated by bru

plot.prediction

Plot prediction using ggplot2

Prediction from fitted bru model

Prediction from fitted inla model

vertices.inla.mesh

Extract vertex locations from an inla.mesh

Plot a globe using rgl

Render Spatial* and inla.mesh objects using RGL

Weighted summation (integration) of data frame subsets

Generate integration points

Likelihood construction for usage with bru

materncov.bands

Matern correlation or covariance function approximate credible bands.