Package: dendRoAnalyst
Type: Package
Title: A Tool for Processing and Analyzing Dendrometer Data
Version: 2.0.0
Date: 2026-09-27
Authors@R: c(
    person("Sugam", "Aryal", email = "sugam.aryal@fau.de", role = c("aut", "cre", "dtc")),
    person("Martin", "Häusser", email = "martin.haeusser@fau.de", role = "aut"),
    person("Jussi", "Grießinger", email = "jussi.griessinger@fau.de", role = "aut"),
    person("Ze-Xin", "Fan", email = "fanzexin@xtbg.org.cn", role = "aut"),
    person("Achim", "Bräuning", email = "achim.braeuning@fau.de", role = c("aut", "dgs")))
Description: 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) <doi:10.1111/nph.70266>.
    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) <doi:10.1111/nph.13995>, and the first version
    of the package is described in Aryal et al. (2020)
    <doi:10.1016/j.dendro.2020.125772>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.1.0)
Imports: stats, tools, utils, tidyverse, dplyr, ggplot2, lubridate,
        readxl, tibble, tidyr, zoo, forecast, mgcv, minpack.lm,
        pspline, moments, signal, readr, boot, rlang, changepoint,
        WaveletComp
Suggests: shiny (>= 1.8.0), bslib (>= 0.7.0), DT, shinyFiles, knitr,
        rmarkdown, writexl, zip, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
Packaged: 2026-10-01 20:21:02 UTC; xe40husu
Author: Sugam Aryal [aut, cre, dtc],
  Martin Häusser [aut],
  Jussi Grießinger [aut],
  Ze-Xin Fan [aut],
  Achim Bräuning [aut, dgs]
Maintainer: Sugam Aryal <sugam.aryal@fau.de>
Repository: CRAN
Date/Publication: 2026-10-02 06:40:02 UTC
