Author: Sugam Aryal
Date: 2026-04-06

# News in latest version of ‘dendRoAnalyst’ package

#### Sugam Aryal

#### 2026-04-06

## What’s new in dendRoAnalyst 0.1.6

This release focuses on speed, clarity, and reproducibility. Many core functions were refactored to be more consistent in their inputs and outputs, several new utilities were added, and plotting responsibilities were split into dedicated helpers. The package now follows a more workflow-oriented design: import, quality control, conditioning, phase detection, climate integration, growth modelling, detrending, and event-based analyses can be run in a more transparent sequence.

The release also strengthens reproducibility by using explicit namespaces where needed, clearer output classes, more dedicated summary/plot methods, and safer handling of gaps, smoothing, and climate joins.

### 1. Core phase engines are now more modular

The two main phase engines concentrate on phase assignment and statistics, while separate helpers handle visualization and climate integration:

- `phase.sc()` applies the Stem-Cycle approach and returns phase-level and cycle-level summaries for shrinkage, expansion, and increment.
- `phase.zg()` applies the Zero-Growth approach and returns timestamp-level, phase-level, and cycle-level summaries for GRO and TWD.
- `twd.maxima()` locates local maxima within TWD phases and helps bridge stem-water-deficit interpretation with phase-based workflows.

### 2. Dedicated plotting methods for phase outputs

Plotting is now separated from computation so users can calculate once and visualize in multiple ways:

- `plot.SC_output()` plots stem-cycle outputs from `phase.sc()`.
- `plot.ZG_output()` plots GRO and TWD outputs from `phase.zg()`.
- `dm_plot_climate()` and `dm_plot_climate_compare()` provide climate-focused views for dendrometer–climate interpretation.
- `plot_event_climate_box()` and `plot_event_climate_relation()` add targeted visualization for event-based climate summaries.

### 3. Expanded data import, checking, and quality control

Several tools now support more robust input handling and early quality checks:

- `read.dendrometer()` reads TXT, CSV, TSV, and Excel dendrometer files with improved datetime parsing.
- `read.climate()` supports climate import in the same workflow style used for dendrometer data.
- `reso_dm()` detects the effective temporal resolution of a time series and flags irregularities.
- `jump.locator()` helps identify jumps or abrupt steps in dendrometer series.
- `i.jump.locator()` provides interactive jump inspection for manual review.

### 4. New and upgraded gap-handling tools

Gap handling has been expanded substantially:

- `dm.na.interpolation()` detects missing timestamps, inserts explicit gaps, interpolates missing values, and can test interpolation performance over multiple gap lengths.
- `plot.dm_na_interpolation()`, `plot_dm_gaps()`, `plot_dm_interpolation()`, and `plot_dm_assessment()` provide dedicated visual diagnostics for missing data and interpolation quality.
- `network.interpolation()` fills gaps in focal dendrometer series using a co-recorded network of reference trees.
- `plot.network_interpolation()` visualizes observed values, interpolated segments, donor-network behavior, and optional uncertainty bands.

Together, these functions make it easier to compare interpolation strategies for both single-tree and site-network workflows.

### 5. Data conditioning tools are broader and clearer

Several conditioning functions were added or refined to make preprocessing more explicit:

- `dendro.resample()` resamples dendrometer or climate data to coarser or finer temporal resolution.
- `dendro.truncate()` truncates a dataset by year and/or day-of-year windows.
- `smooth_dm()` provides compact smoothing options for dendrometer series, including median-mean, penalized spline, Savitzky–Golay, exponential moving average, and loess approaches.
- `dm_standardize()` standardizes dendrometer series for cross-tree comparison.

These functions make it easier to build reproducible preprocessing pipelines before phase detection or modelling.

### 6. Daily summaries are now easier to compute and inspect

The package adds a more structured daily-analysis layer:

- `daily.data()` computes daily minima, maxima, timing of extrema, mean, median, amplitude, lag metrics, and day-to-day status.
- `plot.daily_output()` provides a unified plotting interface for objects returned by `daily.data()`.
- `dm_daily_clim()` summarizes climate variables at the daily scale.
- `dm_subdaily_clim()` derives subdaily climate summaries aligned to higher-frequency analyses.

### 7. Climate joining helpers support both daily and subdaily workflows

Climate can now be attached more directly to dendrometer outputs:

- `dm_join_daily_clim()` joins daily climate summaries to `daily.data()` outputs.
- `dm_join_subdaily_clim()` joins timestamp-level climate features to point-level dendrometer outputs.
- `dm_join_phase_clim()` summarizes climate conditions within phase windows from `phase.sc()` or `phase.zg()`.
- `dm_add_climate()` provides a higher-level helper to enrich dendrometer outputs with climate information.

These functions support more consistent dendrometer–climate analyses across raw, daily, and phase-based outputs.

### 8. Event-based phase and climate tools were added

A new event-oriented layer helps summarize climate conditions during biological or environmental events:

- `dm_event_climate()` extracts climate information for identified events.
- `dm_event_climate_summary()` summarizes climate statistics for event windows.
- `dm_event_climate_test()` supports inferential comparisons among event types or groups.

This makes it easier to move from descriptive phase detection to event-based hypothesis testing.

### 9. Growth modelling is more flexible and easier to evaluate

Growth fitting has been extended well beyond a single legacy curve:

- `dm.fit.gompertz()` remains available as a legacy annual Gompertz option.
- `dm.growth.fit()` fits single-curve models such as GAM, Gompertz, logistic, Richards, loess, and spline by vegetation season.
- `dm.growth.fit.double()` fits bimodal growth trajectories using double Gompertz or double Richards formulations.
- `plot.dm_growth_fit()` provides multiple visual summaries of fitted growth trajectories, timing, residuals, and parameters.
- `dm.growth.evaluate()` compares alternative fitting methods using fit statistics.
- `plot.dm_growth_evaluation()` visualizes method-comparison metrics.
- `print.dm_growth_fit()`, `summary.dm_growth_fit()`, and `print.summary.dm_growth_fit()` improve interpretability of fitted objects.

This workflow makes it easier to compare competing models rather than relying on a single default growth curve.

### 10. Detrending is now better linked to growth fitting

The detrending workflow is more explicit and more tightly integrated with the new growth-fit objects:

- `dm.detrend.fit()` creates detrended standardized series from fitted growth models.
- `plot.dm_detrended()` visualizes original data, fitted curves, residuals, and detrended series.
- `mean_detrended.dm()` aggregates detrended series across trees, with options for robust averaging and autocorrelation-aware summaries.
- `plot.mean_dm_detrended()` visualizes the resulting mean detrended series.

This makes it easier to move from single-tree modelling to species-, site-, or treatment-level detrended signals.

### 11. Superposed epoch analysis now has a fuller toolkit

The package now includes a more complete superposed-epoch workflow:

- `dm_event_times()` helps define event timing.
- `dm_epoch_extract()` extracts windows around those events.
- `dm_epoch_test()` performs superposed epoch analyses.
- `summary.dm_epoch()`, `print.summary.dm_epoch()`, and `plot.dm_epoch()` support inspection and presentation of epoch results.

These functions make it easier to study average dendrometer or climate responses around recurring events.

### 12. Wavelet tools were added for time-frequency analysis

A new wavelet toolkit supports multiscale analysis of dendrometer dynamics:

- `dm_wavelet()` performs wavelet decomposition of dendrometer series.
- `summary.dm_wavelet()`, `print.summary.dm_wavelet()`, and `plot.dm_wavelet()` support interpretation of the decomposition.
- `dm_wavelet_reconstruct()` reconstructs selected components of the wavelet signal.
- `summary.dm_wavelet_reconstruct()`, `print.summary.dm_wavelet_reconstruct()`, and `plot.dm_wavelet_reconstruct()` help evaluate reconstruction output.
- `dm_wavelet_coherence()` analyzes coherence between dendrometer and climate signals across scales.
- `plot.dm_wavelet_coherence()` visualizes coherence structure.

These additions broaden the package from time-domain analyses to multiscale time-frequency exploration.

### 13. Moving climate–growth correlations were upgraded

Running climate–growth relationships now have a more complete summary and plotting framework:

- `mov.cor.dm()` calculates moving correlations between daily dendrometer summaries and climate variables.
- `summary.mov_cor_dm()`, `print.summary_mov_cor_dm()`, and `print.mov_cor_dm()` support object inspection.
- `plot.mov_cor_dm()`, `plot_mov.cor()`, `plot.mov.cor()`, `plot.mov.cor.boot()`, and `plot.summary_mov_cor_dm()` provide multiple ways to visualize running correlations, significance, and peak windows.

### 14. Harsh-climate analysis is more complete

The climate-stress workflow now covers detection, summarization, testing, and plotting:

- `clim.twd()` identifies adverse climate periods and calculates relative dendrometer change.
- `clim.twd.stats` is available as `clim.twd.stats()` for grouped summaries at tree, species, or site level.
- `summary.clim_twd_stats()` and `print.summary.clim_twd_stats()` summarize grouped results.
- `plot.clim_twd_stats()` visualizes grouped climate-stress trajectories and event metrics.
- `clim.twd.test()` adds inferential comparison among groups or treatments.
- `summary.clim_twd_test()`, `print.summary.clim_twd_test()`, and `plot.clim_twd_test()` support reporting and visualization of test results.

This makes harsh-climate analysis more useful for both exploratory and comparative studies.

### 15. Package design now emphasizes compute-first, plot-second workflows

Across the package, many functions now return richer objects with dedicated print, summary, and plot methods. This makes analyses easier to reproduce, compare, and document. In practice, users can now:

- compute a result once,
- inspect the returned object,
- summarize it with dedicated helpers, and
- generate multiple plot types without repeating the analysis.

This separation is especially visible in the phase, growth-fit, moving correlation, harsh-climate, interpolation, and wavelet workflows.

### Heads-up on namespaces

The package now more consistently uses explicit namespace calls internally to avoid clashes such as `stats::filter()` versus `dplyr::filter()`. If you work interactively and use both packages, it can still be helpful to set:

    conflicted::conflict_prefer("filter", "dplyr")

### In short

`dendRoAnalyst` 0.1.6 is no longer just a collection of stand-alone utilities. It now behaves more like a connected analysis framework for dendrometer data, with clearer transitions among import, quality control, interpolation, conditioning, phase classification, climate integration, growth modelling, detrending, event analysis, moving correlations, wavelets, and climate-stress assessment.