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secr (version 2.2.0)

Spatially explicit capture-recapture

Description

Functions to estimate the density 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

2.2.0

License

GPL (>= 2)

Maintainer

Murray Efford

Last Published

October 2nd, 2011

Functions in secr (2.2.0)

SECR Model Predictions

Construct Density Design Data

Initial Parameter Values for SECR

Closed population estimates

Convert Data To Or From BUGS Format

Frequently Asked Questions, And Others

Plot Detection Functions

Area of Polygon(s)

Construct Detection Model Design Matrices and Lookups

Check SECR Data

Ovenbird Mist-netting Dataset

Plot Detection Histories

Deermouse Live-trapping Datasets

Read Habitat Mask From File

Probability Density of Home Range Centre

Home Range Statistics

Distance To Nearest Detector

Drop Unwanted List Components

Plot Habitat Mask

Detector or Mask Spacing

Covariates Attribute

Simulate From Fitted secr Model

Detector Attributes

Profile Likelihood Confidence Intervals

Spatially Explicit Capture--Recapture by Inverse Prediction

Empirical Variance of H-T Density Estimate

Plot traps Object

Plot likelihood surface

Build Habitat Mask

Build Detector Array on Triangular or Hexagonal Grid

Mask Diagnostics

secr.model.detection

Models for Detection Parameters

Mask Buffer Width

Print Detectors

Score Test for SECR Models

Build Detector Array

Spatial Capture History Object

Confidence ellipse

Sort Rows of capthist Object

Combine Columns

secr.make.newdata

Create Default Design Data

Print Detections

Summarise Habitat Mask

Subset popn Object

Combine capthist Objects

summary.capthist

Summarise Detections

Skink Pitfall Data

Variance - Covariance Matrix of SECR Parameters

Spatially Explicit Capture--Recapture Models

Logit Transformation

Dissect Spatial Capture History Object

Simulate Detection Histories

Subset traps Object

make.systematic

Construct Systematic Detector Design

Construct capthist Object

Plot popn Object

Subset Mask Object

Deviance of fitted secr model and residual degrees of freedom

Write Data to Text File

Likelihood Ratio Test for SECR Models

Expected Number of Individuals

Multi-session Objects

reduce.capthist

Combine Occasions Or Convert Detector Types

subset.capthist

Subset or Split capthist Object

Combine popn Objects

SECR Models Fitted to Demonstration Data

Ovenbird Acoustic Dataset

Summarise Detector Array

transformations

Transform Point Array

Derived Parameters of Fitted SECR Model

Detector Clustering

Multinomial Coefficient of SECR Likelihood

Net Detection Probability

Combine traps Objects

Flat-tailed Horned Lizard Dataset

Model averaging for SECR Models

Population Object

Brushtail Possum Trapping Dataset

Read Detector Data From File

Spatially Explicit Capture--Recapture

Compare SECR Models

Import or export data

Circular Probability

Coefficients of secr Object

Exchange data with SPACECAP package

Detection Functions

Mask Buffer Diagnostic Plot

Mask Buffer Diagnostic Plot (internal)

Print secr Object

Simulate 2-D Population

secr.model.density

Complex Detector Layouts

Spatially Explicit Capture--Recapture Models

House mouse live trapping data

Contour Detection Probability

Population Size