Overview | Example datasets | 1. Reading dendrometer and climate data | read.dendrometer() | read.climate() | 2. Looking for jumps or gaps in dendrometer data | reso_dm() | jump.locator() and i.jump.locator() | dm.na.interpolation() and its plot methods | 3. Resample and truncate the dataset | dendro.resample() | dendro.truncate() | 4. Smoothing | smooth_dm() | 5. Daily approach and daily climate functions | daily.data() and plot.daily_output() | dm_daily_clim() and dm_join_daily_clim() | dm_add_climate() for daily outputs | 6. Stem-cycle approach and associated functions | phase.sc() and plot.SC_output() | dm_subdaily_clim(), dm_join_subdaily_clim(), dm_join_phase_clim(), and dm_add_climate() | 7. Zero-growth approach and associated functions | phase.zg(), twd.maxima(), and plot.ZG_output() | Climate joins and climate-phase plots | 8. Superposed epoch and associated functions | dm_event_times(), dm_epoch_extract(), dm_epoch_test(), and method dispatch | 9. Event-based climate functions | 10. Growth fit and evaluation | dm.fit.gompertz() | dm.growth.fit(), plot.dm_growth_fit(), and print/summary methods | dm.growth.fit.double() and dm.growth.evaluate() | 11. Detrending and associated functions | dm_standardize() and plot.dm_standardized() | 12. Moving correlations and associated functions | 13. Harsh climate using clim.twd() and associated functions | 14. Network interpolation | network.interpolation() and plot.network_interpolation() | 15. Wavelet analysis and associated functions | dm_wavelet(), summary.dm_wavelet(), and plot.dm_wavelet() | dm_wavelet_reconstruct() and its methods | dm_wavelet_coherence() and plot.dm_wavelet_coherence() | Closing note | For suggestions, comments and questions please contact : [email protected]
Package: dendRoAnalyst 2.0.0
Sugam Aryal
dendRoAnalyst: 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) <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>.
Authors:
dendRoAnalyst_2.0.0.tar.gz
dendRoAnalyst_2.0.0.zip(r-4.7-any)dendRoAnalyst_2.0.0.zip(r-4.6-any)
dendRoAnalyst_2.0.0.tar.gz(r-4.7-any)dendRoAnalyst_2.0.0.tar.gz(r-4.6-any)
dendRoAnalyst_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
dendRoAnalyst/json (API)
| # Install 'dendRoAnalyst' in R: |
| install.packages('dendRoAnalyst', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- gf_nepa17 - Dendrometer data of Kathmandu for 2017 with gap filled
- ktm_clim_hourly - Hourly climate data for Kathmandu derived from ERA5-Land
- ktm_rain17 - Daily rainfall data of Kathmandu for 2017.
- nepa - Dendrometer data from Kathmandu
- nepa17 - Dendrometer data of Kathmandu for 2017
- nepa2 - Dendrometer data from Kathmandu version 2
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:7dab4ab0f4. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 192 | ||
| source / vignettes | OK | 285 | ||
| linux-release | OK | 216 | ||
| windows-devel | OK | 156 | ||
| windows-release | OK | 153 | ||
| wasm-release | OK | 160 |
Exports:clim.twdclim.twd.statsclim.twd.testdaily.datadendro.resampledendro.truncatedendroanalystdm_add_climatedm_daily_climdm_epoch_extractdm_epoch_testdm_event_climatedm_event_climate_summarydm_event_climate_testdm_event_timesdm_join_daily_climdm_join_phase_climdm_join_subdaily_climdm_plot_climatedm_plot_climate_comparedm_standardizedm_subdaily_climdm_waveletdm_wavelet_coherencedm_wavelet_reconstructdm.detrend.fitdm.fit.gompertzdm.growth.evaluatedm.growth.fitdm.growth.fit.doubledm.na.interpolationi.jump.locatorjump.locatormean_detrended.dmmov.cor.dmnetwork.interpolationphase.scphase.zgplot_dm_assessmentplot_dm_gapsplot_dm_interpolationplot_event_climate_boxplot_event_climate_relationplot_mov.corread.climateread.dendrometerreso_dmsmooth_dmtwd.maxima
Dependencies:askpassbackportsbase64encbitbit64blobbootbroombslibcachemcallrcellrangerchangepointclicliprcolorspaceconflictedcpp11crayoncurldata.tableDBIdbplyrdigestdplyrdtplyrevaluatefarverfastmapfontawesomeforcatsforecastfracdifffsgarglegenericsggplot2gluegoogledrivegooglesheets4gtablehavenhighrhmshtmltoolshttridsisobandjquerylibjsonliteknitrlabelinglatticelifecyclelmtestlubridatemagrittrMASSMatrixmemoisemgcvmimeminpack.lmmodelrmomentsnlmennetopensslotelpillarpkgconfigprettyunitsprocessxprogresspspsplinepurrrR6raggrappdirsRColorBrewerRcppRcppArmadilloreadrreadxlrematchrematch2reprexrlangrmarkdownrstudioapirvestS7sassscalesselectrsignalstringistringrsyssystemfontstextshapingtibbletidyrtidyselecttidyversetimechangetimeDatetinytextzdburcautf8uuidvctrsviridisLitevroomWaveletCompwithrxfunxml2yamlzoo
dendRoAnalyst: end-to-end workflow and function tour
Last update: 2026-10-02
Started: 2020-07-03
News in latest version of 'dendRoAnalyst' package
What's new in dendRoAnalyst 0.1.6 | 1. Core phase engines are now more modular | 2. Dedicated plotting methods for phase outputs | 3. Expanded data import, checking, and quality control | 4. New and upgraded gap-handling tools | 5. Data conditioning tools are broader and clearer | 6. Daily summaries are now easier to compute and inspect | 7. Climate joining helpers support both daily and subdaily workflows | 8. Event-based phase and climate tools were added | 9. Growth modelling is more flexible and easier to evaluate | 10. Detrending is now better linked to growth fitting | 11. Superposed epoch analysis now has a fuller toolkit | 12. Wavelet tools were added for time-frequency analysis | 13. Moving climate–growth correlations were upgraded | 14. Harsh-climate analysis is more complete | 15. Package design now emphasizes compute-first, plot-second workflows | Heads-up on namespaces | In short
Last update: 2026-05-20
Started: 2024-03-20
