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secr (version 4.4.5)

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

4.4.5

License

GPL (>= 2)

Maintainer

Murray Efford

Last Published

July 12th, 2021

Functions in secr (4.4.5)

Compare SECR Models

Coefficient of Variation

Convert Data To Or From BUGS Format

Model Compatibility

Construct Density Design Data

Import or export data

Likelihood Ratio Test

Orongorongo Valley Brushtail Possums

Plot Likelihood Surface

Telemetry Fixes in Polygons

Combine Telemetry and Detection Data

RSE from Fitted Model

Add Covariates to Mask or Traps

Mark-resight Data

Frequently Asked Questions, And Others

Density Surfaces

Circular Probability

Profile Likelihood Confidence Intervals

Closed population estimates

Coerce capthist to Data Frame

Detection Functions

Mask Buffer Diagnostic Plot (internal)

Expected Number of Individuals

Multi-core Processing

Distance To Nearest Detector

Detector Clustering

Multinomial Coefficient of SECR Likelihood

Confidence Ellipses

Contour Detection Probability

Coefficients of secr Object

Convert Data to RMark Input Format

Coerce traps object to mask

Smoothed Resource Surface

Initial Parameter Values for SECR

Deviance of fitted secr model and residual degrees of freedom

Covariates Attribute

House mouse live trapping data

Spatial Capture History Object

Derived Parameters of Fitted SECR Model

First or Last Part of an Object

Construct Lacework Detector Design

Build Habitat Mask

Dissect Spatial Capture History Object

Rasterize Area Search or Transect Data

Detail Specification for secr.fit

Simulated Movements

deleteMaskPoints

Edit Mask Points

Combine or Split Sessions of capthist Object

Mask Buffer Diagnostic Plot

Deermouse Live-trapping Datasets

Flat-tailed Horned Lizard Dataset

Empirical Variance of H-T Density Estimate

Probability Density of Home Range Centre

Activity Centres of Detected and Undetected Animals

Construct capthist Object

Hybrid Mixture Model

Home Range Statistics

Plot Habitat Mask, Density or Resource Surface

Build Detector Array on Triangular or Hexagonal Grid

Mask Diagnostics

Net Detection Probability

Spatially Explicit Capture--Recapture by Inverse Prediction

Work with Open Population data

Ovenbird Acoustic Dataset

make.systematic

Construct Systematic Detector Design

Plot Population Object

Fit Multiple SECR Models

Population Object

Random Landscape

shareFactorLevels

Fix Inconsistent Covariates

Plot Detection Histories

Print Detectors

Build Detector Array

Key to Petal Plot

Brushtail Possum Trapping Dataset

Sort Rows of capthist or mask Object

Sighting Attributes

Detector or Mask Spacing

pointsInPolygon

Points Inside Polygon

Plot Detection Functions

predictDsurface

Predict Density Surface

Read Habitat Mask From File

SECR Model Predictions

Plot traps Object

Import Telemetry Fixes

Area of Polygon(s)

Combine traps Objects

Combine popn Objects

Subset traps Object

Spatially Explicit Capture--Recapture Models

Print Detections

Create a RasterLayer Object from Mask or Dsurface

Mask Buffer Width

Print or Summarise secr Object

Ovenbird Mist-netting Dataset

Construct Detection Model Design Matrices and Lookups

Read Detector Data From File

Averaging of SECR Models Using Akaike's Information Criterion

Logit Transformation

Create Default Design Data

Smooth Terms in SECR Models

Spatially Explicit Capture--Recapture

Multi-session Objects

Population Size

Combine capthist Objects

Score Test for SECR Models

Simulate 2-D Population

Simulate Detection Histories

Slice Transect Into Shorter Sections

rectangularMask

Rectangular Mask

summary.capthist

Summarise Detections

Check SECR Data

Troubleshooting

Problems in Fitting SECR Models

Specifying a Dynamic Population

Write Data to Text File

Outline Around Mask Cells

Random Number Seed

Goodness-of-fit Test Results

Goodness-of-Fit Test

Reformat Signal Data

SECR Models Fitted to Demonstration Data

reduce.capthist

Combine Occasions Or Detectors

Mixture Model Check

Combine Columns

Number of Threads

Colour Strip Legend

Skink Pitfall Data

Simulate From Fitted secr Model

transformations

Transform Point Array

Time-varying Covariates

subset.capthist

Subset or Split capthist Object

Update Old capthist Format

Summarise Simulated Population

Summarise Habitat Mask

Complex Detector Layouts

Summarise Detector Array

Drop Unwanted List Components

Detector Attributes

Subset, Split or Combine Mask Objects

Subset popn Object

Non-Euclidean Distances

Utility Functions

Plot usage, detections or sightings.

Variance - Covariance Matrix of SECR Parameters