Release v0.9

Major update:

• Confidence intervals are no longer calculated in the core indicator workflow, except for Hill diversity (Hill diversity is handled by the iNEXT package, which calculates confidence intervals internally). They are now calculated using a separated function, add_ci(), which can be applied to any indicator_ts object for which reasonable confidence intervals can be calculated. This gives the user more freedom. After adding CIs, you can recalculate with different parameters by setting 'replace = TRUE' when you call add_ci().
• Indicator-specific bootstrap defaults: add_ci() now applies different default behaviors based on indicator type to ensure statistically appropriate confidence intervals:
  • Species-level incidence indicators (spec_occ, spec_range) and aggregate indicators (evenness, rarity, density) use whole-cube bootstrapping to correctly capture changes in species composition.
  • Raw counting indicators (total_occ) use group-specific bootstrapping (resampling strictly within each specific year).
  • Evenness indicators (pielou_evenness, williams_evenness) automatically apply logit transformation to keep confidence intervals within valid [0,1] bounds
  • Total occurrences (total_occ) has bias correction disabled by default
• Bootstrapping for confidence intervals is now done across the entire cube by default. This uses the dubicube package, which is now added as a dependency. The option to calculate at indicator level is still available by setting 'level = "indicator"' when calling add_ci(). Indicator level bootstrapping is a faster but less robust method.
• The default number of bootstrap replicates when calculating confidence intervals is now 1000. This improves robustness at the sake of speed. This can still be changed using the 'num_bootstrap' parameter when calling add_ci().
• Added boot_args and ci_args parameters to add_ci() to allow fine-tuning of the underlying dubicube calls. These can be used to override the indicator-specific defaults if needed.
• Implemented group_specific vs whole_cube resampling logic in add_ci() to handle different indicator calculation requirements. Species-level indicators are automatically detected and handled appropriately.
• Added new internal helper function prepare_indicator_bootstrap() to centralize bootstrap parameter configuration. This function implements a "rule book" pattern that defines appropriate defaults for each indicator type.
• Included detailed bootstrap summary statistics (est_boot, se_boot, bias_boot) in the output of add_ci().
• Added a new conceptual vignette: "Uncertainty in Biodiversity Indicators".
• Added comprehensive unit and integration tests for the decoupled uncertainty workflow.