A Tool for Processing and Analyzing Dendrometer Data

Tools for importing, cleaning, analyzing, and visualizing high-resolution dendrometer data and for linking them with climate data. Dendrometer and climate records can be imported with automatic date-time parsing (read.dendrometer(), read.climate()) and checked for a regular temporal resolution (reso_dm()). Preprocessing functions detect and correct artificial jumps with a threshold-based or an automatic changepoint method (jump.locator()), detect and fill gaps with spline, seasonal, or network interpolation (dm.na.interpolation(), network.interpolation()), and truncate or resample the series (dendro.truncate(), dendro.resample()). Daily statistics (daily.data()), the stem-cycle approach (phase.sc()), and the zero-growth approach (phase.zg()) separate radial growth from reversible stem shrinkage and swelling. The function phase.zg() also returns metrics of tree water deficit (TWD) phases, including the event-based ABr index, and the daily drought indices of Peters et al. (2025) . Climate data can be summarized at daily and sub-daily scales and attached to daily, phase-level, and point-level outputs (dm_add_climate()). Event-based climate analyses, superposed epoch analyses, and adverse-period analyses (dm_event_climate(), dm_epoch_test(), clim.twd()) relate tree responses to climate conditions. Seasonal growth can be fitted with Gompertz, logistic, Richards, generalized additive model, LOESS, and spline functions, detrended, and compared among methods (dm.growth.fit(), dm.detrend.fit(), dm.growth.evaluate()). Running correlations with climate (mov.cor.dm()) and wavelet power and coherence analyses based on 'WaveletComp' (dm_wavelet(), dm_wavelet_coherence()) are also provided. Most outputs have dedicated plot methods, and an optional 'shiny' application (dendroanalyst()) allows the complete workflow to be run without programming. The zero-growth approach follows Zweifel et al. (2016) , and the first version of the package is described in Aryal et al. (2020) .