Changes in version 0.1.0 (2026-06-30)                  

Initial release

Parameter estimation

  - estimate_its_params() — all-in-one wrapper: estimates baseline,
    sigma, rho, and trend_pre jointly by maximum likelihood from a
    single GLS model with AR(1) errors (nlme::gls + nlme::corAR1), with
    an automatic OLS fallback if GLS fails to converge. The estimation
    method used is reported in the returned method element. This matches
    the simulation engine and avoids the upward bias in sigma (and the
    unreliable rho) that the OLS two-step produces under
    autocorrelation. Accepts a data frame or a plain numeric vector;
    handles date-indexed time columns and missing values with
    informative warnings.
  - Individual functions: estimate_baseline(), estimate_sigma(),
    estimate_rho(), estimate_trend(). Each uses a simple OLS two-step
    and is intended for quick single-parameter checks; for power
    calculation, prefer the jointly estimated values from
    estimate_its_params().

Power simulation

  - simulate_its_data() — data-generating process for a single ITS site:
    Gaussian outcome with linear trend, step change, slope change, and
    AR(1) autocorrelated errors. Supports negative rho.
  - fit_its_model() — segmented regression via nlme::gls() with AR(1)
    correction; returns p-value for "level", "slope", or "both" effects.
  - calculate_power() — Monte Carlo power estimation for single-site
    ITS. Returns a pits_power_result object.
  - calculate_power_multi() — power for multi-site designs via
    nlme::lme() with site random intercepts and per-site AR(1). Returns
    a pits_power_result object.
  - power_sweep() — runs calculate_power() across a vector of n_post
    values; returns a pits_sweep_result data frame.
  - build_param_grid() — constructs a factorial parameter grid for
    sensitivity analyses.
  - run_power_grid() — applies calculate_power() to every row of a grid;
    appends power estimates to the grid data frame.

Plots and diagnostics

  - plot_power_curve() — line plot of power vs n_post, with 80% target
    line and minimum adequate duration marker.
  - plot_power_heatmap() — colour-coded power grid across two
    parameters, with cell labels and adequacy marker.
  - plot_its_example() — simulated ITS series with fitted segmented
    regression overlaid; useful for figures in papers and protocols.
  - diagnose_params() — 2×2 diagnostic panel: observed series + trend,
    residuals over time, Q-Q plot, and residual ACF.

S3 methods

  - print.pits_power_result() — formatted console output for
    calculate_power() and calculate_power_multi() results.
  - summary.pits_power_result() — extended output including p-value
    quantile distribution.
  - print.pits_sweep_result() — formatted table with adequacy markers
    for power_sweep() output.

Utilities and wrappers

  - interpret_power() — converts a numeric power estimate to "Adequate
    (>= 80%)", "Borderline (60-79%)", or "Underpowered (< 60%)".
  - validate_params() — pre-flight parameter checks with informative
    errors and warnings.
  - simulate_predata() — generates synthetic pre-intervention data with
    known parameters; useful for testing and vignette examples.
  - export_results() — saves pits_power_result or pits_sweep_result
    objects to timestamped CSV and plain-text summary files. The output
    directory (dir) must be supplied by the user; there is no default
    path, so nothing is ever written to the working directory or home
    filespace unless explicitly requested.
  - run_its_power() — full single-site workflow with console output and
    optional sweep and file saving.
  - estimate_and_calculate() — chains parameter estimation and power
    calculation in a single call.

Data

  - example_cfr_data — 24 months of synthetic monthly case fatality rate
    data (baseline ≈ 14.7%, sigma ≈ 1.51, rho ≈ 0.37) for use in
    vignettes and examples.

Vignettes

  - PITS: Power of an ITS — CDSS/CFR worked example: complete workflow
    from parameter estimation through design optimisation, sensitivity
    analysis, multi-site power, and export.
  - Estimating ITS parameters from pre-intervention data: detailed guide
    to individual and all-in-one estimation functions, diagnostic
    interpretation, and handling of missing data, date columns, and
    short pre-periods.