Changes in version 0.4.0 (2026-07-16)                  

2026-06-20

  - 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".
  - Fixed a major performance bug in 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) model
  - ct_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.

                        Changes in version 0.3.0                        

2025-08-09

  - Added Distance Sampling functions:
    
      - 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.

  - Added Camera Trap Data Package (Camtrap DP) integration:
    
      - 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.

                        Changes in version 0.2.0                        

2025-07-29

Improved ct_stack_df() - C++ implementation for stacking a list of data
frames.

2025-07-10

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.

2025-06-26

  - ct_correct_datetime() to correct datetime stamps in camera trap
    datasets using a deployment-specific correction table. Supports
    multiple datetime formats, offset directions.

2025-06-25

  - 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.

2025-06-24

  - Improved handling of non-numeric variables in ct_describe_df().
  - Added support for detecting sampling breaks using ct_find_break().
  - Added function to compute confidence intervals (ct_ci() and
    ct_lognorm_ci())
  - Fixed NSE-related warnings

First Release Highlights

  - Initial release of maimer
  - Provides tidyverse-friendly functions for data cleaning,
    transformation, and visualization.
  - Includes support for alpha & beta diversity, species activity
    overlap, and temporal analysis.
  - Integrates with ggplot2 for customizable visualizations.
  - Features an interactive Shiny app for image metadata handling