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Type:

Package

Title:

Helper Functions for Package 'secr'

Version:

1.0.0

Date:

2025-11-26

Description:

Functions are provided for internal use by the spatial capture-recapture package 'secr' (from version 5.4.0). The idea is to speed up the installation of 'secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from 'RcppNumerical' and 'RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data.

Depends:

R (≥ 3.5.0)

Imports:

Rcpp (≥ 0.12.14), RcppNumerical, RcppParallel (≥ 5.1.1)

Suggests:

knitr, rmarkdown, testthat

LinkingTo:

BH, Rcpp, RcppEigen, RcppNumerical, RcppParallel

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

SystemRequirements:

GNU make

URL:

https://www.otago.ac.nz/density/

Encoding:

UTF-8

NeedsCompilation:

yes

Packaged:

2025-11-25 23:57:24 UTC; murra

Author:

Murray Efford

[aut, cre], Philipp Jund [ctb] ((faster transect search and spacing))

Maintainer:

Murray Efford <murray.efford@otago.ac.nz>

Repository:

CRAN

Date/Publication:

2025-12-01 14:50:02 UTC

Helper Functions for Package secr

Description

This package provides some functions used internally by the spatial capture-recapture package secr (from version 5.4.0). The idea is to speed up the installation of secr, and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from RcppNumerical (Qiu et al. 2023) and RcppEigen.

The functions are not intended to be user-friendly and require considerable pre-processing of data. Examples and tests use data pre-formatted and saved in the 'extdata' folder.

Details

Package:	secrfunc
Type:	Package
Version:	1.0.0
Date:	2025-11-26
License:	GNU General Public License Version 2 or later

Author(s)

Murray Efford murray.efford@otago.ac.nz

References

Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64, 377–385.

Efford, M. G. (2011) Estimation of population density by spatially explicit capture–recapture with area searches. Ecology 92, 2202–2207.

Efford, M. G. (2025) The SECR book. A handbook of spatially explicit capture–recapture methods. Version 1.0.1. Zenodo doi:10.5281/zenodo.15109938. Online at https://murrayefford.github.io/SECRbook/.

Qiu, Y., Balan, S., Beall, M., Sauder, M., Okazaki, N., and Hahn, T. (2023). RcppNumerical: 'Rcpp' Integration for Numerical Computing Libraries. R package version 0.6-0. https://CRAN.R-project.org/package=RcppNumerical

Net Detection Hazard for Polygon and Transect Detectors

Description

Compute spatially explicit net probability of detection for individual(s) at given coordinates. Used by secr::pdot().

Usage


hdotpolycpp(xy, traps, Tsk, markocc, cumk, detectfn, gsb, convex, dim, grain, ncores)

Arguments

xy

2-column matrix of coordinates

traps

2-column matrix; coordinates of detector polygon vertices

Tsk

detector usage (numeric matrix detectors x occasions)

markocc

integer vector of marking occasion codes (1 = marking)

cumk

integer vector of start indices of vertices for each detector

detectfn

integer detector code (see secr)

gsb

numeric vector of real parameter values (lambda0, sigma)

convex

logical; if TRUE all detector polygons are convex (non-convex untested)

dim

integer; 1 = transect, 2 = polygon

grain

integer grain argument of RcppParallel::parallelFor

ncores

integer number of threads

Details

Uses 'integrate' function from RcppNumerical. Sighting occasions are ignored.

Value

Rcpp NumericVector with one hazard for each row in 'xy'.

Examples


set.seed(123)

# 50 random points
xy <- matrix(runif(100), ncol = 2) * 2 - 1

# centred unit polygon 
traps <- matrix(c(0,0,1,1,0,0,1,1,0,0), ncol = 2) - 0.5

Tsk <- matrix(1, nrow = 1, ncol = 5)
markocc <- rep(1,5)
cumk <- c(0,5,0)   # zero-terminated
detectfn <- 14   # hazard half-normal
gsb <- c(lambda0 = 0.2, sigma = 0.5)

h <- hdotpolycpp(xy, traps, Tsk, markocc, cumk, detectfn, gsb, 
    convex = TRUE, dim = 2, grain = 1, ncores = 2)

Pre-compute Detection Hazard

Description

Hazards and probabilities are computed for each parameter combination, detector and mask point. Used by secr internal functions generalsecrloglik() and secr_makegk().

Usage

makegkPolygoncpp(detectfn, dim, convex, grain, ncores, gsbval, cumk, traps, mask)

Arguments

detectfn

integer detector code (see secr)

dim

integer; 1 = transect, 2 = polygon

convex

logical; if TRUE all detector polygons are convex

grain

integer grain argument of RcppParallel::parallelFor

ncores

integer number of threads

gsbval

matrix of real parameter values, one row per combination

cumk

integer vector of start indices of vertices for each detector

traps

2-column matrix; coordinates of detector polygon vertices

mask

2-column matrix; coordinates of mask points

Value

List with components

H

unbounded integrated hazard from radial function

gk

probability vector for elements of cc x detector x mask array

hk

hazard vector for elements of cc x detector x mask array

Examples


detectfn <- 14   # hazard half-normal
dim <- 2
convex <- TRUE
grain <- 1
ncores <- 2
# for a single parameter combination
gsbval <- matrix(c(0.2,1), nrow = 1)
cumk <- c(0,5)   
# centred unit polygon 
traps <- matrix(c(0,0,1,1,0,0,1,1,0,0), ncol = 2) - 0.5
mask <- as.matrix(expand.grid(x=-2:2, y=-2:2))

makegkPolygoncpp(detectfn, dim, convex, grain, ncores, gsbval, cumk, traps, mask)

Probability of Polygon Capture Histories

Description

polygonhistoriescpp is the core function for the contribution of individual histories to likelihood. It is called by internal secr function generalsecrloglik().

polygonfxicpp is the core code for 'fxi' calculations in secr when the detector type is polygon or transect based. It is called by the internal secr function allhistpolygonfxi.

Usage


polygonhistoriescpp(nc, detectfn, grain, ncores, minp, binomN, w, xy, start, 
    group, hk, H, gsbval, pID, mask, density, PIA, Tsk, h, hindex, debug)
    
polygonfxicpp(nc, detectfn, grain, ncores, minp, binomN, w, xy, start, 
    group, hk, H, gsbval, pID, mask, density, PIA, Tsk, h, hindex)

Arguments

nc

integer number of individuals

detectfn

integer detector code (see secr)

grain

integer grain argument of RcppParallel::parallelFor

ncores

integer number of threads

minp

double minimum probability

binomN

integer vector binomial code

w

3-D integer array of capture histories (individual x occasion x detector)

xy

2-column matrix of detection location coordinates

start

integer vector of index of row in xy for each first detection

group

integer vector; code for group of each individual (zero-based)

hk

numeric array of detection hazards from makegkPolygoncpp

H

numeric unbounded integrated hazard from radial function from makegkPolygoncpp

gsbval

matrix of real parameter values, one row per combination

pID

numeric matrix occasions x nmix. NOT USED?

mask

2-column matrix of mask coordinates

density

matrix relative density at each mask point (row) for each group? (column)

PIA

integer vector for the slice of the parameter index array for a given mixture component and session

Tsk

detector usage (numeric matrix detectors x occasions)

h

for exclusive detectors, pre-computed hazard; otherwise -1

hindex

for exclusive detectors, index to precomputed hazard; otherwise -1

debug

logical; if TRUE...

Details

The capture history 'w' merely records detection/nondetection on a particular occasion. 'xy' records the location of each positive detection.

Value

For polygonhistoriescpp – Numeric vector of probabilities, one per individual.

For polygonfxicpp – Numeric matrix with probability density for each individual (rows) at each mask point (columns).

Examples


datafilename <- system.file("extdata/testdata.RData", package = "secrfunc")
load(datafilename)  # loads w, dimw, xy, start, traps, mask, gsbval, cumk, binomN

detectfn <- 14   # hazard half-normal
dim <- 2
convex <- TRUE
grain <- 1
ncores <- 2

gkhk <- makegkPolygoncpp(detectfn, dim, convex, grain, ncores, gsbval, cumk, traps, mask)
# see tests for expected result

nc      <- dimw[1]  # individuals
S       <- dimw[2]  # occasions
K       <- dimw[3]  # single polygon
minp    <- 1e-200
group   <- rep(0,nc)
pID     <- matrix(1, nrow = S, ncol = 1)
density <- matrix(1/nrow(mask), nrow(mask), 1)
PIA     <- as.integer(array (1, dim = c(nc, S, K)))
Tsk     <- matrix(1, nrow = K, ncol = S)
h       <- matrix(-1)
hindex  <- matrix(-1)
debug   <- FALSE

prw <- polygonhistoriescpp(nc, detectfn, grain, ncores, minp, binomN, w, xy, start, 
    group, gkhk$hk, gkhk$H, gsbval, pID, mask, density, PIA, Tsk, h, hindex, debug)
# see tests for expected result

fxi <- polygonfxicpp(nc, detectfn, grain, ncores, minp, binomN, w, xy, start, 
    group, gkhk$hk, gkhk$H, gsbval, pID, mask, density, PIA, Tsk, h, hindex)
# see tests for expected result

Helper Functions for Package secr

Description

Details

Author(s)

References

See Also

Net Detection Hazard for Polygon and Transect Detectors

Description

Usage

Arguments

Details

Value

Examples

Pre-compute Detection Hazard

Description

Usage

Arguments

Value

Examples

Probability of Polygon Capture Histories

Description

Usage

Arguments

Details

Value

See Also

Examples