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secr (version 2.10.0)

Spatially Explicit Capture-Recapture

Description

Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.

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Version

Install

install.packages('secr')

Monthly Downloads

1,565

Version

2.10.0

License

GPL (>= 2)

Maintainer

Murray Efford

Last Published

December 7th, 2015

Functions in secr (2.10.0)

Telemetry Fixes in Polygons

Hybrid Mixture Model

Orongorongo Valley Brushtail Possums

Derived Parameters of Fitted SECR Model

Compare SECR Models

Construct Density Design Data

Area of Polygon(s)

Dissect Spatial Capture History Object

Density Surfaces

Plot popn Object

Combine or Split Sessions of capthist Object

Mask Buffer Diagnostic Plot

Construct capthist Object

Initial Parameter Values for SECR

Contour Detection Probability

sim.popn details

Specifying a Dynamic Population

Distance To Nearest Detector

Plot likelihood surface

Activity Centres of Detected and Undetected Animals

Home Range Statistics

Exchange data with SPACECAP package

Covariates Attribute

Confidence Ellipses

Detection Functions

Flat-tailed Horned Lizard Dataset

reduce.capthist

Combine Occasions Or Detectors

Simulate Detection Histories

Deviance of fitted secr model and residual degrees of freedom

Colour Strip Legend

Population Object

Spatially Explicit Capture--Recapture Models

Spatially Explicit Capture--Recapture by Inverse Prediction

Closed population estimates

Plot Habitat Mask, Density or Resource Surface

deleteMaskPoints

Edit Mask Points

Convert Data to RMark Input Format

Frequently Asked Questions, And Others

transformations

Transform Point Array

Combine traps Objects

Plot Detection Histories

Convert Data To Or From BUGS Format

Expected Number of Individuals

Combine popn Objects

Import or export data

SECR Models Fitted to Demonstration Data

First or Last Part of an Object

Empirical Variance of H-T Density Estimate

House mouse live trapping data

Ovenbird Mist-netting Dataset

make.systematic

Construct Systematic Detector Design

Fit Multiple SECR Models

Print Detectors

Reformat Signal Data

Spatial Capture History Object

Skink Pitfall Data

Drop Unwanted List Components

Detector or Mask Spacing

Coefficient of Variation

Combine Telemetry and Detection Data

Averaging of SECR Models Using Akaike's Information Criterion

Combine Columns

Ovenbird Acoustic Dataset

Print Detections

Population Size

rectangularMask

Rectangular Mask

Rasterize Area Search or Transect Data

Subset Mask Object

Sort Rows of capthist or mask Object

Subset traps Object

SECR Model Predictions

Score Test for SECR Models

Mark-resight Data

Build Detector Array on Triangular or Hexagonal Grid

Circular Probability

Coefficients of secr Object

Plot Detection Functions

Non-Euclidean Distances

Spatially Explicit Capture--Recapture

Smooth Terms in SECR Models

Summarise Detector Array

summary.capthist

Summarise Detections

Add Covariates to Mask or Traps

Smoothed Resource Surface

Multinomial Coefficient of SECR Likelihood

Profile Likelihood Confidence Intervals

Detector Clustering

Random Landscape

Import Radio Fixes

Goodness-of-fit Test Results

Multi-core Processing

Build Detector Array

Combine capthist Objects

Multi-session Objects

Slice Transect Into Shorter Sections

Probability Density of Home Range Centre

Detail Specification for secr.fit

Deermouse Live-trapping Datasets

secr.model.detection

Models for Detection Parameters

Likelihood Ratio Test

Summarise Habitat Mask

Build Habitat Mask

Write Data to Text File

Complex Detector Layouts

Logit Transformation

Mask Buffer Width

Read Habitat Mask From File

Utility Functions

Read Detector Data From File

Mask Diagnostics

predictDsurface

Predict Density Surface

secr.model.density

Spatially Explicit Capture--Recapture Models

Mixture Model Check

Sighting Attributes

Net Detection Probability

Time-varying Detector Covariates

Plot traps Object

Subset popn Object

Detector Attributes

Troubleshooting

Problems in Fitting SECR Models

Brushtail Possum Trapping Dataset

Mask Buffer Diagnostic Plot (internal)

subset.capthist

Subset or Split capthist Object

pointsInPolygon

Points Inside Polygon

Construct Detection Model Design Matrices and Lookups

Print secr Object

Goodness-of-Fit Test

secr.make.newdata

Create Default Design Data

Simulate 2-D Population

Simulate From Fitted secr Model

Variance - Covariance Matrix of SECR Parameters

Create a RasterLayer Object from Mask or Dsurface

Check SECR Data