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: 2026-10-02 13:05:46 UTC; root
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: https://cran.r-universe.dev
Date/Publication: 2026-10-02 12:51:46 UTC
RemoteUrl: https://github.com/cran/dendRoAnalyst
RemoteRef: HEAD
RemoteSha: 7dab4ab0f4f5a693ac12f14a47b776e32989ce1f
