Learn R Programming

⚠️There's a newer version (5.2.4) of this package.Take me there.

secr (version 2.10.3)

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.

Copy Link

Version

Install

install.packages('secr')

Monthly Downloads

1,470

Version

2.10.3

License

GPL (>= 2)

Maintainer

Murray Efford

Last Published

May 13th, 2016

Functions in secr (2.10.3)

Exchange data with SPACECAP package

Deermouse Live-trapping Datasets

Detection Functions

Construct capthist Object

Distance To Nearest Detector

Compare SECR Models

Mask Buffer Diagnostic Plot (internal)

Convert Data To Or From BUGS Format

Construct Detection Model Design Matrices and Lookups

Frequently Asked Questions, And Others

Likelihood Ratio Test

Spatial Capture History Object

Plot traps Object

Detail Specification for secr.fit

Orongorongo Valley Brushtail Possums

Confidence Ellipses

Summarise Detector Array

secr.model.density

Expected Number of Individuals

Probability Density of Home Range Centre

Combine Telemetry and Detection Data

Utility Functions

Plot popn Object

Multinomial Coefficient of SECR Likelihood

Sort Rows of capthist or mask Object

Covariates Attribute

Simulate 2-D Population

Subset popn Object

Circular Probability

Mask Diagnostics

Spatially Explicit Capture--Recapture by Inverse Prediction

secr.model.detection

Models for Detection Parameters

Outline Around Mask Cells

Mark-resight Data

Construct Density Design Data

Plot Habitat Mask, Density or Resource Surface

Convert Data to RMark Input Format

Initial Parameter Values for SECR

Hybrid Mixture Model

Combine capthist Objects

Population Size

Dissect Spatial Capture History Object

Detector or Mask Spacing

predictDsurface

Predict Density Surface

Print Detectors

Ovenbird Acoustic Dataset

Mask Buffer Diagnostic Plot

Reformat Signal Data

Combine or Split Sessions of capthist Object

Smooth Terms in SECR Models

Subset traps Object

Activity Centres of Detected and Undetected Animals

Sighting Attributes

Logit Transformation

subset.capthist

Subset or Split capthist Object

pointsInPolygon

Points Inside Polygon

Spatially Explicit Capture--Recapture

Mixture Model Check

Print secr Object

Check SECR Data

Plot Detection Histories

SECR Models Fitted to Demonstration Data

Skink Pitfall Data

summary.capthist

Summarise Detections

make.systematic

Construct Systematic Detector Design

Averaging of SECR Models Using Akaike's Information Criterion

Combine traps Objects

Random Landscape

Combine Columns

Read Detector Data From File

Simulate From Fitted secr Model

rectangularMask

Rectangular Mask

Non-Euclidean Distances

Read Habitat Mask From File

Import or export data

Density Surfaces

Coefficients of secr Object

Variance - Covariance Matrix of SECR Parameters

Spatially Explicit Capture--Recapture Models

Deviance of fitted secr model and residual degrees of freedom

Goodness-of-Fit Test

sim.popn details

Specifying a Dynamic Population

Net Detection Probability

secr.make.newdata

Create Default Design Data

Complex Detector Layouts

Goodness-of-fit Test Results

reduce.capthist

Combine Occasions Or Detectors

Flat-tailed Horned Lizard Dataset

Slice Transect Into Shorter Sections

Drop Unwanted List Components

Plot Detection Functions

Write Data to Text File

Mask Buffer Width

Coefficient of Variation

transformations

Transform Point Array

Detector Attributes

Telemetry Fixes in Polygons

Build Habitat Mask

Print Detections

Troubleshooting

Problems in Fitting SECR Models

Combine popn Objects

Subset Mask Object

Population Object

Spatially Explicit Capture--Recapture Models

Import Radio Fixes

Ovenbird Mist-netting Dataset

Smoothed Resource Surface

Multi-core Processing

Detector Clustering

deleteMaskPoints

Edit Mask Points

House mouse live trapping data

Fit Multiple SECR Models

Simulate Detection Histories

Time-varying Detector Covariates

Build Detector Array on Triangular or Hexagonal Grid

Plot likelihood surface

Add Covariates to Mask or Traps

Closed population estimates

Contour Detection Probability

Derived Parameters of Fitted SECR Model

Brushtail Possum Trapping Dataset

SECR Model Predictions

Area of Polygon(s)

First or Last Part of an Object

Score Test for SECR Models

Rasterize Area Search or Transect Data

Empirical Variance of H-T Density Estimate

Home Range Statistics

Build Detector Array

Multi-session Objects

Create a RasterLayer Object from Mask or Dsurface

Colour Strip Legend

Summarise Habitat Mask

Profile Likelihood Confidence Intervals