Package {soReta}

Title:	Ready-to-Analyze Datasets from Camera Trap Data
Version:	0.1.0
Description:	Functions to build datasets ready for statistical analysis from camera trap data: GLMM/GAMM on counts/RAI at various temporal levels, group size, occupancy, kernel/circular analysis of activity patterns, temporal interactions between species, hierarchical diel models, and classic capture-mark-recapture. Input data must be formatted in the style produced by 'camtrapR', the standard convention in the field.
License:	GPL (≥ 3)
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
Imports:	dplyr, lubridate, tidyr
Config/roxygen2/version:	8.1.0
Suggests:	camtrapR, GLMMadaptive, knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
LazyData:	true
URL:	https://github.com/OrlandoTomassini/soReta
BugReports:	https://github.com/OrlandoTomassini/soReta/issues
NeedsCompilation:	no
Packaged:	2026-09-03 09:47:18 UTC; orlan
Author:	Orlando Tomassini [aut, cre]
Maintainer:	Orlando Tomassini <orlando.tomassini@gmail.com>
Repository:	CRAN
Date/Publication:	2026-09-12 14:10:03 UTC

Convert an individual x occasion table (or a list per species) into CMR capture-history strings

Description

Convert an individual x occasion table (or a list per species) into CMR capture-history strings

Usage

as_capture_strings(ch, na_symbol = ".")

Arguments

Value

if ch is a data.frame: a character vector with names = Individual, values = capture-history string (e.g. "1.010"). If ch is a list: a named list per species, same structure per element.

Build an N-day-block individual x block capture history (classic CMR)

Description

Build an N-day-block individual x block capture history (classic CMR)

Usage

build_cmr_block(
  recordTable,
  camOp,
  block_days,
  individualCol = "Individual",
  speciesCol = "Species",
  stationCol = "Station",
  dateTimeCol = "DateTimeOriginal",
  min_days = 1
)

Arguments

Value

If speciesCol is present: named list, one data.frame per species. Otherwise: a single data.frame. Each data.frame has an Individual column and one column per block of the period covered by camOp ("YYYY-MM-DD–YYYY-MM-DD"), values 0/1/NA as described above.

Build a daily individual x day capture history (classic CMR)

Description

Build a daily individual x day capture history (classic CMR)

Usage

build_cmr_day(
  recordTable,
  camOp,
  individualCol = "Individual",
  speciesCol = "Species",
  stationCol = "Station",
  dateTimeCol = "DateTimeOriginal"
)

Arguments

Value

If speciesCol is present: named list, one data.frame per species (result[["SpeciesName"]]). Otherwise: a single data.frame. Each data.frame has an Individual column and one column per truly active day covered by camOp ("YYYY-MM-DD"), values 0/1 only (see Details – no NA case at this daily grain).

Build a dataset with 1 row per calendar day, aggregating across all stations

Description

Build a dataset with 1 row per calendar day, aggregating across all stations

Usage

build_day_total(
  recordTable,
  camOp,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Date, n_stations_active (number of stations active that day), N_sp (number of species detected that day, across all stations), and for each species two columns: "<species>_N" (sum of independent events across all stations active that day) and "<species>_RAI" (= N / n_stations_active * 100, rounded to 2 decimals).

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a binomial site x N-day-block x time-bin x species dataset

Description

Build a binomial site x N-day-block x time-bin x species dataset

Usage

build_diel_binomial_block(
  recordTable,
  camOp,
  block_days,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  bin_hours = 1,
  min_days = NULL
)

Arguments

Value

data.frame with Station, block_start, block_end, n_days_active, Time, Species, success, failure, Site_Block.

Build a binomial site x month x time-bin x species dataset

Description

Build a binomial site x month x time-bin x species dataset

Usage

build_diel_binomial_month(
  recordTable,
  camOp,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  bin_hours = 1,
  min_days = NULL
)

Arguments

Value

data.frame with Station, sampling_event ("YYYY-MM"), n_days_active, Time, Species, success, failure, Site_Event.

Build a binomial site x period/season x time-bin x species dataset

Description

Build a binomial site x period/season x time-bin x species dataset

Usage

build_diel_binomial_period(
  recordTable,
  camOp,
  period_names,
  period_starts,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  bin_hours = 1,
  min_days = NULL
)

Arguments

Value

data.frame with Station, period_name, year, period_start ("DD/MM"), period_end ("DD/MM"), n_days_active, Time, Species, success, failure, Site_Period.

Build a non-aggregated, day-by-day site x day x time-bin x species dataset

Description

Build a non-aggregated, day-by-day site x day x time-bin x species dataset

Usage

build_diel_day(
  recordTable,
  camOp,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  bin_hours = 1,
  tz = "UTC"
)

Arguments

Value

data.frame with columns Station, Date (Date class, real day, always a truly active day for that station), date (POSIXct class, local noon of Date in the tz time zone. Time (start of the time bin, 0-24), Species, detected (0/1).

Build a dataset with 1 row per calendar day, with group size statistics across all stations

Description

Build a dataset with 1 row per calendar day, with group size statistics across all stations

Usage

build_group_day_total(
  recordTable,
  camOp,
  countCol,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Date, n_stations_active (number of stations active that day), N_sp (number of species detected that day, across all stations), and for each species four columns: "<species>_mean_group_size", "<species>_max_group_size", "<species>_sum_group_size", "<species>_RAI_individuals" (= sum_group_size / n_stations_active * 100, rounded to 2 decimals). NA (not 0) if the species had no events, at any station, that day.

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a site x N-day-block dataset with group size statistics per species

Description

Build a site x N-day-block dataset with group size statistics per species

Usage

build_group_size_block(
  recordTable,
  camOp,
  countCol,
  block_days,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  min_days = NULL,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, block_start, block_end, n_days_active, N_sp (number of species with at least one event in the block), and for each species four columns: "<species>_mean_group_size", "<species>_max_group_size", "<species>_sum_group_size", and "<species>_RAI_individuals" (= sum_group_size / n_days_active * 100, rounded to 2 decimals). NA (not 0) if the species had no events in that block.

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a site x day dataset with group size statistics per species

Description

Build a site x day dataset with group size statistics per species

Usage

build_group_size_day(
  recordTable,
  camOp,
  countCol,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, Date, N_sp (number of species with at least one event that day), and for each species present in recordTable three columns: "<species>_mean_group_size", "<species>_max_group_size", "<species>_sum_group_size" – computed across that species' independent events on that day. NA (not 0) if the species had no events that day. Includes only the days on which the station was active in camOp.

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a dataset with 1 row per independent event, including group size

Description

Build a dataset with 1 row per independent event, including group size

Usage

build_group_size_events(
  recordTable,
  camOp,
  countCol,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, Species, DateTime (start of the event's bout), Date (the calendar day of DateTime), Time ("HH:MM:SS", the time of day of DateTime), group_size (the MAXIMUM value of countCol across all photos in that bout – never the sum, to avoid double-counting the same individuals photographed more than once within one passage). Includes only events that fell on a truly active day for that station.

Build a site x month dataset with group size statistics per species

Description

Build a site x month dataset with group size statistics per species

Usage

build_group_size_month(
  recordTable,
  camOp,
  countCol,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  min_days = NULL,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, year, month, month_start, month_end, n_days_active, n_days_in_month, N_sp (number of species with at least one event that month), and for each species four columns: "<species>_mean_group_size", "<species>_max_group_size", "<species>_sum_group_size", "<species>_RAI_individuals" (= sum_group_size / n_days_active * 100, rounded to 2 decimals). NA (not 0) if the species had no events that month.

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a site x period/season dataset with group size statistics per species

Description

Build a site x period/season dataset with group size statistics per species

Usage

build_group_size_period(
  recordTable,
  camOp,
  countCol,
  period_names,
  period_starts,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, period_name, year, period_start ("DD/MM", no year), period_end ("DD/MM", no year), period_length_days, n_days_active, N_sp (number of species with at least one event in that period), and for each species four columns: "<species>_mean_group_size", "<species>_max_group_size", "<species>_sum_group_size", "<species>_RAI_individuals" (= sum_group_size / n_days_active * 100, rounded to 2 decimals). NA (not 0) if the species had no events in that period.

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a dataset with 1 row per site, with group size statistics over the entire activity period

Description

Build a dataset with 1 row per site, with group size statistics over the entire activity period

Usage

build_group_size_total(
  recordTable,
  camOp,
  countCol,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, n_days_active, N_sp (number of species with at least one event over the entire period), and for each species four columns: "<species>_mean_group_size", "<species>_max_group_size", "<species>_sum_group_size", "<species>_RAI_individuals" (= sum_group_size / n_days_active * 100, rounded to 2 decimals). NA (not 0) if the species was never detected at that station.

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build, for each species, a site x N-day-block detection history

Description

Build, for each species, a site x N-day-block detection history

Usage

build_occupancy_block(
  recordTable,
  camOp,
  block_days,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  min_days = 1
)

Arguments

Value

named list, one data.frame per species. Each data.frame has a Station column and one column per block of the period covered by camOp (column name = "YYYY-MM-DD–YYYY-MM-DD", block start and end), values 0/1/NA as described above.

Build, for each species, a daily site x day detection history

Description

Build, for each species, a daily site x day detection history

Usage

build_occupancy_day(
  recordTable,
  camOp,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal"
)

Arguments

Value

named list, one data.frame per species. Each data.frame has a Station column and one column per day of the period covered by camOp (column name = date "YYYY-MM-DD"), values 0/1/NA as described above.

Build a site x N-day-block dataset with independent-event counts per species

Description

Build a site x N-day-block dataset with independent-event counts per species

Usage

build_site_block(
  recordTable,
  camOp,
  block_days,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  min_days = NULL,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, block_start, block_end, mid_day (day halfway between block_start and block_end, handy for joining external daily covariates), n_days_active, N_sp (number of species with at least one event in the block), and for each species present in recordTable two columns: "<species>_N" (independent-event count) and "<species>_RAI" (Relative Abundance Index = count / n_days_active * 100, rounded to 2 decimals).

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a site x day dataset with independent-event counts per species

Description

Build a site x day dataset with independent-event counts per species

Usage

build_site_day(
  recordTable,
  camOp,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, Date, N_sp (number of species with at least one event that day), and one "<species>_N" column (independent-event count) per species present in recordTable. No "<species>_RAI" columns (redundant at the daily level – see above). Includes only the days on which the station was active in camOp.

Build a site x month dataset with independent-event counts per species

Description

Build a site x month dataset with independent-event counts per species

Usage

build_site_month(
  recordTable,
  camOp,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  min_days = NULL,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, year, month, month_start, month_end, mid_day (day halfway between month_start and month_end, handy for a join with external daily covariates), n_days_active, n_days_in_month, N_sp (number of species with at least one event in the month), and for each species two columns: "<species>_N" (independent-event count) and "<species>_RAI" (Relative Abundance Index = count / n_days_active * 100, rounded to 2 decimals).

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a site x period/season dataset with independent-event counts per species

Description

Build a site x period/season dataset with independent-event counts per species

Usage

build_site_period(
  recordTable,
  camOp,
  period_names,
  period_starts,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  min_days = NULL,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, period_name, year, period_start ("DD/MM", no year), period_end ("DD/MM", no year), mid_day (a REAL Date, with year – the day halfway between the start and end of the period, handy for a join with external daily covariates), period_length_days, n_days_active, N_sp (number of species with at least one event in the period), and for each species two columns: "<species>_N" (independent-event count) and "<species>_RAI" (Relative Abundance Index = count / n_days_active * 100, rounded to 2 decimals).

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Build a dataset with 1 row per site, aggregating over the entire activity period

Description

Build a dataset with 1 row per site, aggregating over the entire activity period

Usage

build_site_total(
  recordTable,
  camOp,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

data.frame with columns Station, n_days_active (total active days over the entire period), N_sp (number of species with at least one event over the entire period), and for each species two columns: "<species>_N" (independent-event count over the entire period) and "<species>_RAI" (= N / n_days_active * 100, rounded to 2 decimals).

Note

Species names in the columns are cleaned (spaces and non-alphanumeric characters replaced by "_") via the internal .sanitize_species_names().

Compute the AA/ABA and BB/BAB intervals between two species (Parsons et al. 2016, T3/T4)

Description

Compute the AA/ABA and BB/BAB intervals between two species (Parsons et al. 2016, T3/T4)

Usage

build_species_pair_interruptions(
  recordTable,
  speciesA,
  speciesB,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

long data.frame with columns Station, type ("AA", "ABA", "BB" or "BAB"), time_from (start of the interval) and delta_hours (duration). For the T4/T3 comparison of Parsons et al., extract the vectors with: AA <- out$delta_hours[out$type == "AA"] ABA <- out$delta_hours[out$type == "ABA"] (and likewise for BB/BAB).

Compute the AB/BA intervals between two species (Niedballa et al. 2019 method), with censoring

Description

Compute the AB/BA intervals between two species (Niedballa et al. 2019 method), with censoring

Usage

build_species_pair_intervals(
  recordTable,
  camOp,
  speciesA,
  speciesB,
  stationCol = "Station",
  speciesCol = "Species",
  dateTimeCol = "DateTimeOriginal",
  threshold_min = 30,
  max_gap_hours = NULL,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Value

long data.frame with columns Station, direction ("AB" or "BA"), time_from (timestamp of the starting event), delta_hours (hours until the next event of the other species, or until the censoring limit) and censored (TRUE if there was no subsequent event before the limit – end of monitoring or max_gap_hours). For the Niedballa comparison, extract the two vectors with: AB <- out$delta_hours[out$direction == "AB"] BA <- out$delta_hours[out$direction == "BA"] (and decide yourself whether to include or exclude rows with censored == TRUE).

Example camera effort matrix for soReta

Description

A station x day effort matrix, built once from camtraps_soReta via camtrapR::cameraOperation(), and bundled directly with the package: using soReta's example data no longer requires camtrapR at all, since this object is already the finished result of that step. Any station x day matrix in this shape works equally well with every function in this package; cameraOperation() is simply the standard, convenient way to build one from deployment/retrieval/malfunction dates – you are not required to use camtrapR to build your own.

Usage

camOp_soReta

Format

A matrix with 5 rows (stations, matching camtraps_soReta) and 120 columns (one per calendar day from 01/01/2026 to 30/04/2026, column names "YYYY-MM-DD"). Values are 1 (camera active that day), 0 (camera not yet deployed, already retrieved, or down due to a malfunction period), or NA (before deployment/after retrieval, camtrapR's convention for "no camera present at all" as opposed to "present but not working").

Source

Synthetic data generated for this package; see data-raw/create_sample_data.R for the full generation script (fixed random seed, fully reproducible; requires camtrapR to regenerate, though not to use the resulting object).

See Also

camtraps_soReta, the station table this matrix was built from.

Example station table for soReta

Description

A small, entirely SYNTHETIC camera-trap station table, in the format expected by camtrapR::cameraOperation(). Built specifically as the example dataset for this package, so it never needs to depend on camtrapR's own sample data (which lacks an individual-count column and uses different species codes across examples).

Usage

camtraps_soReta

Format

A data frame with 5 rows and 7 columns:

Station

station ID, "S_01" to "S_05".

Setup_date

deployment date, "DD/MM/YYYY".

Retrieval_date

retrieval date, "DD/MM/YYYY". All stations share the same retrieval date, 30/04/2026.

Problem1_from,Problem1_to

start/end of the first malfunction period for that station, "DD/MM/YYYY", or "" if none.

Problem2_from,Problem2_to

start/end of a second malfunction period, "DD/MM/YYYY", or "" if none. Only stations S_03 and S_05 have a second problem period in this example.

Source

Synthetic data generated for this package; see data-raw/create_sample_data.R for the full generation script.

See Also

recordTable_soReta, the matching example recordTable.

Extract, per species (and optionally per one or more groupings), a vector of times in radians

Description

Extract, per species (and optionally per one or more groupings), a vector of times in radians

Usage

extract_radians(
  recordTable,
  dateTimeCol = "DateTimeOriginal",
  stationCol = "Station",
  speciesCol = "Species",
  threshold_min = 30,
  group_cols = c(stationCol, speciesCol),
  group_col = NULL,
  independence_method = c("chain", "window"),
  require_uninterrupted = FALSE
)

Arguments

Details

The radians are in CLOCK TIME, not solar time. For comparisons across seasons or different latitudes, convert the result with activity::solartime() or overlap::sunTime() before passing it to densityPlot()/fitact() – this function does not do that.

Value

Named list per species. If group_col is NULL: result[[species]] is a numeric vector of radians. If group_col has 1 element: result[[species]][[group]]. If it has 2 or more: one further nested level per column, in the same order as group_col.

Create a separate object in the environment for each vector in a nested list of radians

Description

Create a separate object in the environment for each vector in a nested list of radians

Usage

radians_to_env(
  radians_list,
  prefix = "Rad_",
  sep = "_",
  envir = parent.frame()
)

Arguments

Value

(invisible) the vector of names of the created objects. The function is called for its side effect of creating the objects in envir, not for its return value.

Example individually-identifiable recordTable for soReta

Description

A small, entirely SYNTHETIC individual-level recordTable, in the format expected by build_cmr_day()/build_cmr_block() (the same shape as camtrapR's own recordTableIndividual() output). Built by taking the "red deer" detections already present in recordTable_soReta and assigning each one to one of 8 individually-recognizable deer, with unequal, realistic recapture rates (a few "resident" individuals seen often, others seen only once or twice) – purely for illustration, not derived from any real survey. Unlike the CMR examples elsewhere in camtrapR-based tutorials, this dataset requires no dependency on camtrapR's own bundled data.

Usage

recordTableIndividuals_soReta

Format

A data frame with 103 rows and 4 columns:

Station

station ID, matching camtraps_soReta.

Species

always "red deer" in this dataset.

Individual

individual ID, "RD_01" to "RD_08".

DateTimeOriginal

date and time of the photo, POSIXct (UTC).

Source

Synthetic data generated for this package; see data-raw/create_individuals_dataset.R for the full generation script (fixed random seed, fully reproducible; run after data-raw/create_sample_data.R, since it starts from that script's recordTable_soReta object).

See Also

recordTable_soReta, the recordTable this dataset's red deer detections were taken from; camtraps_soReta, the matching example station table.

Example recordTable for soReta

Description

A small, entirely SYNTHETIC camtrapR-style recordTable: four species (wolf, red fox, wild boar, red deer), detections spread across the five stations in camtraps_soReta, only on days those stations were actually active. Detection times are weighted towards night and twilight hours. Individual counts (N_individuals) follow different distributions per species – red fox and red deer mostly solitary (max 2 and 4 respectively), wolf up to small-pack sizes (max 8), wild boar in larger sounders on average (max 30) – purely for illustration, not derived from any real survey.

Usage

recordTable_soReta

Format

A data frame with 404 rows and 4 columns:

Station

station ID, matching camtraps_soReta.

DateTimeOriginal

date and time of the photo, POSIXct (UTC).

Species

one of "wolf", "red fox", "wild boar", "red deer".

N_individuals

number of animals visible in that photo.

Details

Detections are clustered into "visits" of 1 to 4 photos rather than spread one-per-day, so that most active days have no detection at all, while the days that do often have several photos close together in time – including two deliberately placed sequences (one for "wolf" at station S_02, one for "wild boar" at station S_03) with gaps of exactly 16 and 25 minutes between consecutive photos: with the default 30-minute threshold, independence_method = "chain" collapses each of these into a single event, while "window" splits it into two – worked examples for the vignette's discussion of independence_method/ require_uninterrupted.

Source

Synthetic data generated for this package; see data-raw/create_sample_data.R for the full generation script (fixed random seed, fully reproducible).

See Also

camtraps_soReta, the matching example station table.