ct_fit_encounter_response(): tests whether a response
species is detected more or less often at a camera in the hours after a
trigger species passes. Comes with print() and
summary() methods.ct_plot_calendar(): calendar heatmap of daily
detections.ct_fit_distribution(): fits a Poisson, negative
binomial or binomial distribution to counts and reports AIC and
BIC.lama: detections and deployments from 23 cameras in the
Lama Classified Forest, Benin, with distances and angles.penessoulou: image records from the Penessoulou
Classified Forest, Benin. Replaces the two CSV files previously in
inst/.ct_fit_ds(): much faster bootstrap for binned distance
data (fast_bootstrap = TRUE, the default).ct_correct_datetime(): new time_zone
argument, and optional end times to correct clock drift linearly.ct_overlap_matrix(): bootstrap confidence intervals
(n_boot, conf, ci_method,
cores).ct_describe_df(): new by_group
argument.ct_plot_overlap(): new species_names and
legend_title arguments; rug ticks are now coloured by
species (rug_color to override).ct_plot_camtrap_activity(): new
legend_title argument.ct_check_location(): columns can be given
unquoted.ct_spatial_coverage(),
ct_survey_design(), ct_solartime() and
ct_check_location() ask for them when called.ct_temporal_shift() now also returns
Displacement (in hour): the signed shift of the activity
window along the day, measured at its midpoint (positive = later,
negative = earlier). This captures a pure time shift, which
Shift size (a change in window duration) reports
as ~0.ct_temporal_shift() gains period_names and
legend_title arguments to set the legend labels
(e.g. c("Dry", "Rainy")) and legend title directly, instead
of the fixed “First period”/“Second period”/“Period”.ct_fit_ds()
bootstrapping. Distance::bootdht() re-resolves a model’s
symbolic call arguments with parent.frame(n = 3), which
misfires because ct_fit_ds() calls it from one stack frame
deeper: arguments such as cutpoints failed to resolve, so
each bootstrap replicate silently dropped the distance binning and fell
back to the far slower exact-distance likelihood (observed ~19x
slowdown, e.g. ~25 min vs ~1.3 min for one replicate). The model’s
stored call is now frozen to literal values before bootstrapping, so the
bootstrap refits on the intended binned data.ct_fit_ds() gains a seed argument.ct_fit_ds() now shows a progress bar with an ETA during
bootstrapping when the progress package is installed and
n_cores == 1. When n_cores > 1, it reports
up front that live progress is unavailable (a Distance
limitation), so a long parallel run is not mistaken for a freeze.ct_fit_rest() Fit the Random Encounter and Staying Time
(REST / RAD-REST) modelct_fit_tte(), ct_fit_ste(), and
ct_fit_ise() for Time To Event (TTE), Space To EVent (STE),
and Instantaneous Sampling Estimator (ISE) respectively for
density/abundance estimation.ct_fit_ds() for fitting detection functions and
estimating density/abundance.ct_availability() for temporal availability
corrections.ct_QAIC(), ct_chi2_select(), and
ct_select_model() for automated two-stage model
selection.ct_dp_read() to load Camtrap DP datasets from local
files or URLs.ct_dp_table() to access specific tables
(observations, deployments,
media, events, taxa).ct_dp_example() to load example dataset.ct_dp_version() to retrieve dataset standard
version.ct_dp_filter() to subset tables using
dplyr-style filtering.Improved ct_stack_df() - C++ implementation for stacking
a list of data frames.
Added new functions to support trap rate and REM-based density
estimation workflows: ct_traprate_estimate() estimates trap
rates from detection data; ct_fit_activity() models diel
activity patterns; ct_fit_speedmodel() fits animal movement
speed models; ct_fit_detmodel() estimates detection
probability functions; ct_fit_rem() applies the Random
Encounter Model (REM) to estimate animal density;
ct_get_effort() calculates sampling effort metrics such as
camera-days; and ct_traprate_data() prepares detection and
effort data for further analysis.
ct_correct_datetime() to correct datetime stamps in
camera trap datasets using a deployment-specific correction table.
Supports multiple datetime formats, offset directions.ct_plot_camtrap_activity() function to visualize camera
trap deployment activity with optional gap indicators.ct_summarise_camtrap_activity() function to compute
summary statistics for camera trap deployment activity, including active
durations, gaps, and activity rates, etc.ct_describe_df().ct_find_break().ct_ci()
and ct_lognorm_ci())