Showing 3 of total 3 results (show query)
femiguez
nlraa:Nonlinear Regression for Agricultural Applications
Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function 'nlsLMList' uses 'nlsLM' for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples.
Maintained by Fernando Miguez. Last updated 11 months ago.
agriculturalecologynonlinear-mixed-modelsnonlinear-regression
23 stars 8.19 score 112 scripts 2 dependents
jorischau
gslnls:GSL Multi-Start Nonlinear Least-Squares Fitting
An R interface to weighted nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Multi-start optimization based on quasi-random samples is implemented using a modified version of the algorithm in Hickernell and Yuan (1997, OR Transactions). Robust nonlinear regression can be performed using various robust loss functions, in which case the optimization problem is solved by iterative reweighted least squares (IRLS). Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.
Maintained by Joris Chau. Last updated 3 months ago.
gnu-scientific-librarygsllevenberg-marquardtmulti-startnonlinear-least-squaresnonlinear-regressionrobust-regresssionfortranglibc
16 stars 6.23 score 35 scripts 2 dependents
onofriandreapg
drcSeedGerm:Utilities for Data Analyses in Seed Germination/Emergence Assays
Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 <doi:10.1016/j.eja.2018.08.011>. Contains several datasets for practicing.
Maintained by Andrea Onofri. Last updated 3 months ago.
nonlinear-regressionseed-germination-assaystime-to-event
5 stars 3.97 score 37 scripts