The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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())These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.