A compilation of nonlinear growth models.

Assists in the plotting and functional smoothing of traits 
    measured over time and the extraction of features from these traits, 
    implementing the SET (Smoothing and Extraction of Traits) method 
    described in Brien et al. (2020) Plant Methods, 16. Smoothing of 
    growth trends for individual plants using natural cubic smoothing 
    splines or P-splines is available for removing transient effects and 
    segmented smoothing is available to deal with discontinuities in 
    growth trends. There are graphical tools for assessing the adequacy 
    of trait smoothing, both when using this and other packages, such as 
    those that fit nonlinear growth models. A range of per-unit (plant, 
    pot, plot) growth traits or features can be extracted from the 
    data, including single time points, interval growth rates and other 
    growth statistics, such as maximum growth or days to maximum growth. 
    The package also has tools adapted to inputting data from 
    high-throughput phenotyping facilities, such from a Lemna-Tec 
    Scananalyzer 3D (see <https://www.youtube.com/watch?v=MRAF_mAEa7E/> 
    for more information). The package 'growthPheno' can also be 
    installed from <http://chris.brien.name/rpackages/>.

A nonparametric empirical Bayes method for recovering
        gradients (or growth velocities) from observations of smooth
        functions (e.g., growth curves) at isolated time points.

A collection of methods to determine growth rates from
    experimental data, in particular from batch experiments and
    plate reader trials.

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.