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secr (version 4.3.3)

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

4.3.3

License

GPL (>= 2)

Maintainer

Murray Efford

Last Published

December 11th, 2020

Functions in secr (4.3.3)

Construct Density Design Data

Density Surfaces

Plot Likelihood Surface

Compare SECR Models

Likelihood Ratio Test

Convert Data To Or From BUGS Format

Coefficient of Variation

Frequently Asked Questions, And Others

Import or export data

Model Compatibility

RSE from Fitted Model

Orongorongo Valley Brushtail Possums

Multi-core Processing

Telemetry Fixes in Polygons

Convert Data to RMark Input Format

Coerce capthist to Data Frame

Combine Telemetry and Detection Data

Circular Probability

Mark-resight Data

Add Covariates to Mask or Traps

Closed population estimates

Detector Clustering

Coefficients of secr Object

Smoothed Resource Surface

Detection Functions

Initial Parameter Values for SECR

Coerce traps object to mask

Dissect Spatial Capture History Object

House mouse live trapping data

Spatial Capture History Object

Deermouse Live-trapping Datasets

Activity Centres of Detected and Undetected Animals

First or Last Part of an Object

Detail Specification for secr.fit

Derived Parameters of Fitted SECR Model

Covariates Attribute

Hybrid Mixture Model

Mask Buffer Diagnostic Plot

Empirical Variance of H-T Density Estimate

Spatially Explicit Capture--Recapture by Inverse Prediction

Profile Likelihood Confidence Intervals

Combine or Split Sessions of capthist Object

Build Habitat Mask

make.systematic

Construct Systematic Detector Design

Probability Density of Home Range Centre

Build Detector Array on Triangular or Hexagonal Grid

Build Detector Array

Contour Detection Probability

Confidence Ellipses

Logit Transformation

Distance To Nearest Detector

Plot traps Object

Multinomial Coefficient of SECR Likelihood

Plot Detection Histories

Mask Diagnostics

Averaging of SECR Models Using Akaike's Information Criterion

Plot Habitat Mask, Density or Resource Surface

Outline Around Mask Cells

Fit Multiple SECR Models

Net Detection Probability

Rasterize Area Search or Transect Data

deleteMaskPoints

Edit Mask Points

Print Detectors

Deviance of fitted secr model and residual degrees of freedom

Work with Open Population data

Print or Summarise secr Object

Home Range Statistics

Flat-tailed Horned Lizard Dataset

Mask Buffer Diagnostic Plot (internal)

Expected Number of Individuals

Mixture Model Check

predictDsurface

Predict Density Surface

Ovenbird Mist-netting Dataset

Ovenbird Acoustic Dataset

SECR Model Predictions

Brushtail Possum Trapping Dataset

Print Detections

Multi-session Objects

Construct capthist Object

Construct Lacework Detector Design

Key to Petal Plot

pointsInPolygon

Points Inside Polygon

rectangularMask

Rectangular Mask

Plot Population Object

Random Landscape

Create a RasterLayer Object from Mask or Dsurface

Population Object

Plot Detection Functions

Combine traps Objects

Combine capthist Objects

Area of Polygon(s)

Import Telemetry Fixes

Read Detector Data From File

Read Habitat Mask From File

Combine Columns

Combine popn Objects

Spatially Explicit Capture--Recapture

SECR Models Fitted to Demonstration Data

secr.make.newdata

Create Default Design Data

Score Test for SECR Models

Construct Detection Model Design Matrices and Lookups

Random Number Seed

Spatially Explicit Capture--Recapture Models

Population Size

reduce.capthist

Combine Occasions Or Detectors

Goodness-of-Fit Test

Goodness-of-fit Test Results

Number of Threads

shareFactorLevels

Fix Inconsistent Covariates

Sighting Attributes

Simulate Detection Histories

Reformat Signal Data

Smooth Terms in SECR Models

Skink Pitfall Data

Detector or Mask Spacing

subset.capthist

Subset or Split capthist Object

Subset, Split or Combine Mask Objects

transformations

Transform Point Array

Slice Transect Into Shorter Sections

Sort Rows of capthist or mask Object

Complex Detector Layouts

Time-varying Covariates

Simulate 2-D Population

Summarise Detector Array

Plot usage, detections or sightings.

Specifying a Dynamic Population

Simulate From Fitted secr Model

Colour Strip Legend

Summarise Habitat Mask

Summarise Simulated Population

Mask Buffer Width

Subset popn Object

Troubleshooting

Problems in Fitting SECR Models

summary.capthist

Summarise Detections

Drop Unwanted List Components

Update Old capthist Format

Subset traps Object

Write Data to Text File

Detector Attributes

Variance - Covariance Matrix of SECR Parameters

Non-Euclidean Distances

Utility Functions

Check SECR Data