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secr (version 2.9.0)

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

1,692

Version

2.9.0

License

GPL (>= 2)

Maintainer

Murray Efford

Last Published

September 22nd, 2014

Functions in secr (2.9.0)

Plot likelihood surface

Confidence Ellipses

Closed population estimates

Multi-core Processing

Activity Centres of Detected and Undetected Animals

Simulate 2-D Population

Coefficient of Variation

Probability Density of Home Range Centre

Likelihood Ratio Test

Density Surfaces

Print secr Object

Contour Detection Probability

pointsInPolygon

Points Inside Polygon

Distance To Nearest Detector

Mask Buffer Width

Population Size

First or Last Part of an Object

Convert Data To Or From BUGS Format

Ovenbird Mist-netting Dataset

Detector Clustering

Detector or Mask Spacing

Build Habitat Mask

Combine capthist Objects

Home Range Statistics

Multi-session Objects

rectangularMask

Rectangular Mask

Logit Transformation

make.systematic

Construct Systematic Detector Design

Mask Buffer Diagnostic Plot

predictDsurface

Predict Density Surface

Plot Habitat Mask, Density or Resource Surface

Covariates Attribute

Spatial Capture History Object

Summarise Detector Array

Build Detector Array

secr.model.detection

Models for Detection Parameters

Plot traps Object

Reformat Signal Data

SECR Model Predictions

Combine Columns

Expected Number of Individuals

Fit Multiple SECR Models

Combine popn Objects

Slice Transect Into Shorter Sections

Combine traps Objects

Construct Density Design Data

Combine or Split Sessions of capthist Object

Orongorongo Valley Brushtail Possums

Plot popn Object

Add Covariates to Mask or Traps

Telemetry Fixes in Polygons

Spatially Explicit Capture--Recapture by Inverse Prediction

House mouse live trapping data

Mask Buffer Diagnostic Plot (internal)

Detail Specification for secr.fit

Exchange data with SPACECAP package

Compare SECR Models

Smoothed Resource Surface

Initial Parameter Values for SECR

Dissect Spatial Capture History Object

Deermouse Live-trapping Datasets

Derived Parameters of Fitted SECR Model

Deviance of fitted secr model and residual degrees of freedom

secr.make.newdata

Create Default Design Data

Detector Attributes

Construct Detection Model Design Matrices and Lookups

Subset Mask Object

Flat-tailed Horned Lizard Dataset

Troubleshooting

Problems in Fitting SECR Models

Complex Detector Layouts

Build Detector Array on Triangular or Hexagonal Grid

Subset popn Object

Summarise Habitat Mask

Write Data to Text File

Simulate Detection Histories

Subset traps Object

Sort Rows of capthist Object

Multinomial Coefficient of SECR Likelihood

Population Object

Ovenbird Acoustic Dataset

Spatially Explicit Capture--Recapture Models

Plot Detection Functions

Goodness-of-Fit Test

Colour Strip Legend

Spatially Explicit Capture--Recapture Models

Print Detectors

secr.model.density

Random Landscape

Time-varying Detector Covariates

Simulate From Fitted secr Model

Spatially Explicit Capture--Recapture

Mask Diagnostics

subset.capthist

Subset or Split capthist Object

Convert Data to RMark Input Format

Combine Telemetry and Detection Data

Coefficients of secr Object

Profile Likelihood Confidence Intervals

Import Radio Fixes

Goodness-of-fit Test Results

Detection Functions

reduce.capthist

Combine Occasions Or Detectors

Drop Unwanted List Components

Circular Probability

Frequently Asked Questions, And Others

Empirical Variance of H-T Density Estimate

Hybrid Mixture Model

Construct capthist Object

Net Detection Probability

Area of Polygon(s)

Read Habitat Mask From File

SECR Models Fitted to Demonstration Data

Utility Functions

summary.capthist

Summarise Detections

Import or export data

Averaging of SECR Models Using Akaike's Information Criterion

Plot Detection Histories

Print Detections

Score Test for SECR Models

Read Detector Data From File

Skink Pitfall Data

Smooth Terms in SECR Models

Check SECR Data

transformations

Transform Point Array

Variance - Covariance Matrix of SECR Parameters

Brushtail Possum Trapping Dataset