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secr (version 3.0.1)

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

2,295

Version

3.0.1

License

GPL (>= 2)

Maintainer

Murray Efford

Last Published

May 3rd, 2017

Functions in secr (3.0.1)

Compare SECR Models

Coefficient of Variation

Construct Density Design Data

Likelihood Ratio Test

Orongorongo Valley Brushtail Possums

Frequently Asked Questions, And Others

Import or export data

Density Surfaces

Convert Data to RMark Input Format

Smoothed Resource Surface

Spatial Capture History Object

Rasterize Area Search or Transect Data

Dissect Spatial Capture History Object

Distance To Nearest Detector

Confidence Ellipses

Mark-resight Data

Combine Telemetry and Detection Data

Derived Parameters of Fitted SECR Model

Detail Specification for secr.fit

Fit Multiple SECR Models

Net Detection Probability

Plot popn Object

Plot Detection Functions

Import Telemetry Fixes

Read Detector Data From File

Telemetry Fixes in Polygons

Multi-core Processing

Detector Clustering

Coefficients of secr Object

House mouse live trapping data

Covariates Attribute

Combine or Split Sessions of capthist Object

Brushtail Possum Trapping Dataset

SECR Model Predictions

secr.model.detection

Models for Detection Parameters

secr.make.newdata

secr.model.density

Spatially Explicit Capture--Recapture Models

Subset popn Object

Subset traps Object

Detector Attributes

Hybrid Mixture Model

First or Last Part of an Object

Construct capthist Object

sim.popn details

Specifying a Dynamic Population

Time-varying Detector Covariates

transformations

Transform Point Array

Coerce traps object to mask

Initial Parameter Values for SECR

Profile Likelihood Confidence Intervals

Detection Functions

Activity Centres of Detected and

Home Range Statistics

Flat-tailed Horned Lizard Dataset

Troubleshooting

Problems in Fitting SECR Models

Update Old capthist Format

Probability Density of Home Range Centre

Logit Transformation

Key to Petal Plot

Population Object

Random Landscape

Create a RasterLayer Object from Mask or Dsurface

rectangularMask

Rectangular Mask

Averaging of SECR Models Using Akaike's Information Criterion

Mixture Model Check

pointsInPolygon

Points Inside Polygon

Print secr Object

Combine Columns

Goodness-of-Fit Test

SECR Models Fitted to Demonstration Data

shareFactorLevels

Fix Inconsistent Covariates

Combine traps Objects

Read Habitat Mask From File

Sighting Attributes

Detector or Mask Spacing

Print Detectors

Goodness-of-fit Test Results

Slice Transect Into Shorter Sections

Non-Euclidean Distances

Write Data to Text File

Deermouse Live-trapping Datasets

deleteMaskPoints

Edit Mask Points

Build Habitat Mask

Build Detector Array on Triangular or Hexagonal Grid

Plot Detection Histories

Plot Habitat Mask, Density or Resource Surface

predictDsurface

Predict Density Surface

Print Detections

Utility Functions

Score Test for SECR Models

Spatially Explicit Capture--Recapture Models

Construct Detection Model Design Matrices and Lookups

Spatially Explicit Capture--Recapture

Simulate Detection Histories

Simulate 2-D Population

Colour Strip Legend

make.systematic

Build Detector Array

Ovenbird Mist-netting Dataset

Ovenbird Acoustic Dataset

Plot traps Object

Outline Around Mask Cells

Combine capthist Objects

Combine popn Objects

reduce.capthist

Combine Occasions Or Detectors

Population Size

Summarise Habitat Mask

Summarise Detector Array

Variance - Covariance Matrix of SECR Parameters

Skink Pitfall Data

Smooth Terms in SECR Models

subset.capthist

Subset or Split capthist Object

Subset Mask Object

summary.capthist

Summarise Detections