pep725 1.1.0

Bug fixes

• pheno_plot_timeseries() no longer mutates the caller’s data. Previously a setDT(data) call would convert a caller-supplied data.frame into a data.table by reference.

• pheno_trend_turning() — the internal sequential_mk() helper now implements the published Sneyers (1975) sequential Mann-Kendall procedure: cumulative rank-count statistic with and , plus turning-point detection as a sign change of inside the 95% non-significance band. Previous versions used a per-index (non-cumulative) statistic with and an ad-hoc threshold — neither matching Sneyers’ method. Output of pheno_trend_turning() therefore changes numerically for v1.1.0. Vectorised rank counting replaces the previous O(n²) R loop.

• calc_daylength() gains a method argument. The default "brock" matches pep725 <= 1.0.2 (Brock 1981 / Spencer 1971 simple declination formula). A new "cbm" option implements the Forsythe et al. (1995) Climate-Budget- Model formulation, which includes the orbital-eccentricity correction and is typically more accurate near the poles (up to ~15 min/day difference at mid-latitudes). The references section now correctly attributes the default formula to Brock / Spencer; the Forsythe citation is retained for the optional CBM method.

• calc_thermal_units(method = "single_sine") now implements the correct Baskerville-Emin (1969) single-sine formula. Previous versions multiplied the day-above-base width factor incorrectly and returned GDD values that were roughly double the textbook reference for the symmetric-around-base case (e.g., tmin=0, tmax=10, tbase=5 produced 3.18 where the textbook value is 1.59). Analyses that used the single-sine method on PEP725 data in pep725 <= 1.0.2 are affected and should be re-run. New pinned-numeric tests lock the formula against Snyder (1985) and Baskerville-Emin (1969) reference values, plus the single_sine <= modified invariant.

• pheno_normals() now derives quantile column names from the supplied probs levels. Previously custom probs were rejected unless exactly six values were supplied, and even then the output columns were hard-coded to q05..q95 regardless of the actual levels — silently mislabelling quantiles. Default probs still produce q05, q10, q25, q75, q90, q95; custom probs now produce e.g. q02_5, q97_5 for the 2.5% / 97.5% quantiles. The mapping is stored in attr(result, "q_names"), and plot.pheno_normals() falls back to the nearest available quantile pair when the default q25/q75 are not present.

Deprecations

• kendall_tau() is deprecated in favour of mann_kendall_z(). The old function was misnamed: it returned the Mann-Kendall Z-statistic, not Kendall’s τ (which is bounded in [-1, 1]). kendall_tau() still works (now as a thin wrapper that emits a deprecation warning) and returns the same value it always did; please update callers to mann_kendall_z().

New features and improvements

• pep_plot_outliers(type = "diagnostic") — paper/vignette-ready 4-panel diagnostic figure (residuals vs fitted, Q-Q, |residual| vs covariate, per-station max-residual map). Works for any detection method.

• pep_outliers_leaflet() — new function. Interactive Leaflet visualisation of a pep_outliers object: stations as circle markers sized by number of flagged observations and coloured by their maximum absolute residual (or robust Mahalanobis distance for method = "mahalanobis"). Per-station popups list the top-N worst offenders. Complements pheno_leaflet() (which is a selection gadget); this is a visualisation you can embed in knitr reports.

• pep_flag_outliers() gains a multivariate detection method: method = "mahalanobis". Treats each station-year as a vector of DOYs across phases and flags station-years whose robust Mahalanobis distance (MCD estimator via robustbase::covMcd()) exceeds a χ²-based cut-off (default sqrt(qchisq(0.975, df = p))). Catches joint inconsistencies across phases — e.g. a BBCH 60 and 65 reported only one day apart — that slip past every univariate detector because each marginal DOY looks fine. Falls back to the 30-day rule for small or singular groups.

• pep_flag_outliers() gains a model-based detection method: method = "gam_residual". For each group (typically species × phase), fits a GAM of DOY on year, altitude, latitude, and a station random intercept (customisable via the new formula argument) and flags observations whose robust-z-scored residual exceeds threshold (default 3.5). Detects covariate-inconsistent anomalies — e.g. a lowland station reporting a high-altitude DOY, or a station-year report inconsistent with the global climate trend — that the univariate 30-day rule misses. Falls back to "30day" for groups smaller than the new min_n_per_group argument (default 50) or when the GAM fails to converge, with an informative message.

• New mann_kendall_z() function — the correctly named replacement for kendall_tau(). Adds a tie correction to Var(S) (so tied DOY values do not inflate the statistic) and the continuity correction Z = (S - sign(S))/sqrt(Var(S)), matching Kendall::MannKendall(). The vectorised implementation replaces the previous O(n²) R loop.

pep725 1.0.2

• Reduced example sizes for pep_simulate() and pep_coverage() to avoid high CPU/elapsed time ratios on multi-core CRAN check machines

pep725 1.0.1

• Fixed CRAN additional check: pep_download() no longer writes to user cache directory during R CMD check (uses tempdir() instead to avoid _R_CHECK_THINGS_IN_OTHER_DIRS_ note)

pep725 1.0.0

• Initial JOSS and CRAN submission on Valentin`s day, 14. February 2026
• Data access: pep_download(), pep_import(), pep_simulate()
• Core analysis: pheno_normals(), pheno_anomaly(), pheno_gradient(), pheno_synchrony(), pheno_combine(), pheno_trend_turning()
• Quality and validation: pep_quality(), pep_completeness(), pep_check_phases(), pep_flag_outliers(), pep_second_events()
• Advanced analysis: pheno_pls(), calc_thermal_units(), calc_daylength()
• Visualization: plot() methods for all result classes, pheno_leaflet(), pheno_map()
• Four vignettes covering data access, analysis, spatial patterns, and quality assessment