r4pde 0.2.3

New features

• New sad_* family for the validation of standard area diagrams (SADs), for studies with a single SAD (without and with the SAD) and for comparisons of two or more SADs or conditions:
  • sad_data() organises estimates in long or wide format and checks them.
  • Conventional two-stage workflow: sad_ccc() (Lin’s concordance and its components per rater), sad_compare() (means by condition with Tukey letters, mixed model with rater random when raters are paired), sad_reliability() (ICC(A,1) with confidence interval and OCC) and sad_plots() (exploratory figures).
  • One-stage hierarchical model: sad_fit() (glmmTMB mixed model with rater intercepts and slopes per method, image difficulty and severity-dependent dispersion; gaussian or ordered beta), sad_curve() (bias, variance components and expected absolute error along the severity range, and differences between conditions with simultaneous bands; type = "smooth" fits a functional GAMM of the error curves), sad_regions(), sad_raters(), plot_sad_raters(), sad_images() and sad_simulate() (power for planning a study).
• New dataset SADRustSoybean (Franceschi et al. 2020) and vignette “Validation of standard area diagrams”.
• glmmTMB, emmeans, lmerTest, multcompView, MASS and ggridges are suggested packages, needed only by the sad_* functions.
• functional_curves() gains env_smooth and ge_term (with k_ge). env_smooth = TRUE replaces the environment random intercept with a random environment factor-smooth, s(time, env, bs = "fs"), which adjusts for environments that differ in epidemic timing or shape and for treatments that are unbalanced across environments. ge_term = "intercept" or "smooth" adds a genotype-by-environment random intercept or factor-smooth per treatment x environment curve. Defaults keep the previous model. k_env now sets the basis dimension of the environment factor-smooth. Both factor-smooths use a first-order penalty (m = 1), so an environment or G x E deviation flattens out where a curve has no assessments instead of bending back up at the start of the time domain.
• Treatment mean curves average environment factor-smooth and G x E deviations over environments instead of dropping them, because factor-smooths are not centred. The same construction is shared by functional_rate() and diagnose_curves(), so fitted rates match the curves.
• functional_suppression_profiles() gains env_smooth, ge_term and k_ge, passed to functional_curves() in the joint analysis across environments. ge_term = "intercept" or "smooth" adds a treatment-by-environment random intercept or factor-smooth per suppression curve, as already available for functional_instability(). The treatment mean suppression curves average those deviations over the environments where each treatment was observed. Defaults keep the previous model. With by_environment = TRUE the terms are ignored, with a warning.

Bug fixes

• functional_instability() now predicts genotype-by-environment curves with the G x E term, averages out plot-level unit effects, and no longer fails when functional_curves() was fitted with block or unit. It warns when the model has no G x E term, because nFI then mainly reflects each genotype’s disease level.
• functional_distances() (curve level) and functional_resistance(group_method = "bootstrap") supply all model variables when predicting curve-level trajectories; the bootstrap in functional_resistance() previously failed when an environment was modelled.
• functional_distances(): perm_unit is now honoured. Labels are permuted between permutation units (for example genotypes) and mapped back to their curves, instead of being shuffled between curves; this matters when test_factor is a genotype-level property such as a resistance class. perm_strata is applied at the unit level when strata are constant within units; otherwise whole units are permuted without strata, with a warning.
• functional_distances() gains seed (default 1) and, like simulate_dsp_data(seed = 123), no longer resets the user’s random stream: the previous .Random.seed is restored afterwards. The bootstrap seed (boot_seed) is handled the same way.
• functional_resistance(instability = ) accepts the list returned by functional_instability(return_curves = TRUE) (it read a non-existent nfi_table element) and joins on the treatment column instead of guessing it.
• functional_pca_clusters(choose_k = "silhouette" | "elbow") is implemented; it previously used k = 2 silently. The criterion table is attached as attribute "k_selection".
• reconstruct_curves() multiplies back by the column scaling when functional_pca(scale = TRUE) was used, and returns numeric times.
• AFSD(): the summary mean shape index now averages the per-focus SIF = rows / cols (it averaged cols / rows).
• functional_contrast() averages replicated observations explicitly, with a message, instead of letting approx() average ties silently, and reports when constant extrapolation is used.
• functional_summary(): persistence is the total time above threshold, summed over sampling intervals with the profile above the threshold at both ends; gaps below the threshold are no longer counted. Results for profiles without gaps are unchanged.
• fit_gradients(): results_table is a data frame with numeric columns; parameters are no longer rounded before back-transformation or before drawing the fitted curves. The documentation no longer claims the models are ranked.
• theme_r4pde() works without cowplot (in Suggests), falling back to a theme_classic()-based equivalent.
• Documentation: FHBWheat$i and $n are the diseased and assessed plant counts used by BPL(); BlastWheat$index_mean is the wheat blast index.
• Tests: the CompMuCens() test checked a non-existent element.
• functional_suppression_profiles() works when the treatment column is not named "treatment"; the column name was hard-coded in the cluster table and in a join (also in print()).
• functional_suppression_profiles() with environment: the metrics are computed from the fitted suppression curves returned to the scale of response (they were on the 0-1 proportion scale used internally by functional_curves()). threshold and the metrics now have the same units with and without environment; previously the default threshold = 5 gave persistence = 0 for every treatment with percent data. protected_area and max_suppression from joint fits on percent data are now 100 times larger than before.

r4pde 0.2.2

New functions

• Added functional_rate(): Estimates instantaneous rates of plant disease progress (first derivative of trajectory over time) and associated uncertainty from epidemic trajectories fitted by functional_curves(). Supports response and link scale derivatives via GAM linear predictor matrix differences, handles boundary conditions without extrapolation, accounts for zero-epidemic curves and negative rates, and computes key rate phenotypes (r_max, t_r_max, growth duration, cumulative positive growth, and growth windows). Includes S3 methods: print(), summary(), plot(), and augment().

Bug fixes and improvements

• functional_curves(): Fixed smooth term label inspection during prediction so that .unit experimental unit and block random effects are correctly identified and supplied to predict.gam().

r4pde 0.2.1 (2026-06-14)

New functions

• Added a suite of tools for Disease Suppression Profiles (DSP) to evaluate fungicide efficacy over time: functional_contrast(), functional_summary(), plot_dsp(), rank_dsp(), plot_dsp_rank_heatmap(), and simulate_dsp_data().

Bug fixes and improvements

• functional_curves(): Updated the GAM formula to fit independent smooths per treatment by default (trt + s(time, by=trt)), avoiding artifactual drops (“caimento”) at the tails of flat curves. Added the global_smooth parameter to allow users to opt-in to the previous Global-Specific smoothing behavior.
• simulate_dsp_data(): Adjusted the amplitudes of the simulated suppression profiles (“Early”, “Late”, “Persistent”) to achieve exactly a 70% reduction in AUDPC compared to the unsprayed control, representing a realistic fungicide efficacy scenario.
• Updated _pkgdown.yml to include the newly exported functional PCA and DSP functions in the “Functional Analysis” reference index, resolving site build errors.

r4pde 0.2.0 (2026-04-26)

Major changes

• Introduced a modular functional-analysis workflow based on functional_curves(), functional_distances(), functional_pca(), functional_resistance(), and related methods.

New functions

• functional_pca(): Performs functional principal component analysis on fitted disease progress curves to decompose variation among epidemic trajectories into orthogonal temporal components. Includes plotting and extractor functions.
• functional_curves(): Fits genotype-specific epidemic trajectories using GAM, with support for genotype-level covariates.
• functional_distances(): Computes pairwise functional distances among fitted curves and performs hierarchical clustering and permutation testing.
• functional_resistance(): Calculates Functional Resistance Index (FRI) and Stability-Adjusted Functional Resistance Index (SAFRI) with support for stratified rankings and bootstrap-supported classification.
• suggest_k(): Helper function to recommend GAM smoothing parameters (k_smooth, k_trt, k_env, gamma) based on data structure; supports tidy-eval column names and can infer replication from a data frame.

Enhancements

• Added support for genotype-level auxiliary covariates (e.g., heading_group) to adjust functional curves and resistance rankings, allowing for better distinction between genetic resistance and phenological escape.
• Improved bootstrap methodology for resistance classification with support for stratified group comparisons.
• Updated diagnose_curves(), plot_curves(), and plot_dendrogram() to support the new functional analysis object classes.

r4pde 0.1.2 (2026-04-03)

New functions

• Added functional_instability() for computing normalized functional instability (NFI) of genotype-by-environment epidemic curves, with optional decomposition into spatial and temporal components.
• Added plot_functional_instability() for visualizing overall, spatial, and temporal NFI results as bar charts.

r4pde 0.1.1 (2026-02-05)

Enhancements

• Added get_era5() for retrieving ERA5 reanalysis weather data via Open-Meteo API.
• Added get_brdwgd() for extracting daily weather data from the Brazilian Daily Weather Gridded Data (BR-DWGD) NetCDF files.
• Added plotting and diagnostic helpers for curve models.

r4pde 0.1.0 (2025-06-21)

First release

• Initial release of the r4pde package.