Package: growthTrendR 0.3.0.9000

growthTrendR: Toolkit for Data Processing, Quality, and Statistical Models

Offers tools for data formatting, anomaly detection, and classification of tree-ring data using spatial comparisons and cross-correlation. Supports flexible detrending and climate–growth modeling via generalized additive mixed models (Wood 2017, ISBN:978-1498728331) and the 'mgcv' package (<https://CRAN.R-project.org/package=mgcv>), enabling robust analysis of non-linear trends and autocorrelated data. Provides standardized visual reporting, including summaries, diagnostics, and model performance. Compatible with '.rwl' files and tailored for the Canadian Forest Service Tree-Ring Data (CFS-TRenD) repository (Girardin et al. (2021) <doi:10.1139/er-2020-0099>), offering a comprehensive and adaptable framework for dendrochronologists working with large and complex datasets.

Authors:Xiao Jing Guo [aut, cre], Martin Girardin [aut], Juha Metsaranta [aut], David Gervais [aut], Elizabeth Campbell [aut]

growthTrendR_0.3.0.9000.tar.gz
growthTrendR_0.3.0.9000.zip(r-4.7)growthTrendR_0.3.0.9000.zip(r-4.6)growthTrendR_0.3.0.9000.zip(r-4.5)
growthTrendR_0.3.0.9000.tgz(r-4.6-any)growthTrendR_0.3.0.9000.tgz(r-4.5-any)
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growthTrendR_0.3.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
growthTrendR/json (API)

# Install 'growthTrendR' in R:

install.packages('growthTrendR', repos = c('https://cfs-treering.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cfs-treering/growthtrendr/issues

On CRAN:

4.00 score 11 scripts 158 downloads 21 exports 60 dependencies

Last updated from:eb81e4c090. Checks:8 ERROR, 1 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	ERROR	310
source / vignettes	ERROR	327
linux-release-x86_64	ERROR	302
macos-release-arm64	ERROR	201
macos-oldrel-arm64	ERROR	212
windows-devel	ERROR	244
windows-release	ERROR	240
windows-oldrel	ERROR	231
wasm-release	OK	151

Exports:bam_spatial calc_bai CFS_format CFS_freq CFS_mapping CFS_qa CFS_scale ci_resp gam_mod gamm_radius gamm_site gamm_spatial generate_report plot_freq plot_mapping plot_qa plot_resp plot_scale prepare_samples_clim read_rwl sterm_imp

Dependencies:base64enc class classInt cli codetools cpp11 curl data.table DBI digest dplyr e1071 farver fastmap furrr future generics ggplot2 globals glue gtable htmltools isoband KernSmooth labeling lattice lifecycle listenv magrittr MASS Matrix mgcv nlme parallelly patchwork pillar pkgconfig proxy purrr R6 raster RColorBrewer Rcpp rlang s2 S7 scales sf sp stringi stringr terra tibble tidyselect units utf8 vctrs viridisLite withr wk

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

Help page	Topics
spatial growth model for large dataset or vast geographical coverage	bam_spatial
calculate annual basal area increment	calc_bai
Convert tree-ring data into CFS-TRenD format	CFS_format
frequency distributions by geo-location per species	CFS_freq
Interpolate and Map Tree-Ring Data	CFS_mapping
Quality assessment: radius alignment	CFS_qa
Quality assessment: ring-width measurement at plot level	CFS_scale
Compute prediction and confidence intervals of smooth terms on response scale	ci_resp
generalized additive model	gam_mod
detrending model on tree-ring width series	gamm_radius
growth model at site-level	gamm_site
spatial growth model at regional-level (multiple sites)	gamm_spatial
Generate Automated Reports	generate_report
plot frequency distribution by geo-location per species	plot_freq
Plot spatially interpolated tree-ring growth maps	plot_mapping
plotting quality assessment per radius	plot_qa
Plot smooth terms prediction with confidence intervals	plot_resp
plot median of ring width of the target site and its neighbours	plot_scale
Prepare tree-ring data for downstream analysis	prepare_samples_clim
Import raw ring-width measurements from the ITRDB	read_rwl
variable importance of smooth terms in a GAM model	sterm_imp

Package: growthTrendR 0.3.0.9000

growthTrendR: Toolkit for Data Processing, Quality, and Statistical Models

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