Provides tools for working with nonlinear least squares problems.
It is intended to eventually supersede the 'nls()' function in the R
distribution. For example, 'nls()' specifically does NOT deal with small
or zero residual problems as its Gauss-Newton method frequently stops with
'singular gradient' messages. 'nlsr' is based on the now-deprecated package
'nlmrt', and has refactored functions and R-language symbolic derivative
features.

minpack.lm, optimx, Rvmmin, Rcgmin, numDeriv, knitr,
rmarkdown, Ryacas, Deriv

minpack.lm, optimx, numDeriv, knitr, rmarkdown, markdown,
Ryacas, Deriv

Functions for Nonlinear Least Squares Solutions - Updated 2022
Provides tools for working with nonlinear least squares problems.
For the estimation of models reliable and robust tools than nls(), where the
the Gauss-Newton method frequently stops with 'singular gradient' messages.
This is accomplished by using, where possible, analytic derivatives to compute
the matrix of derivatives and a stabilization of the solution of the estimation
equations. Tools for approximate or externally supplied derivative matrices
are included. Bounds and masks on parameters are handled properly.
minpack.lm, optimx, numDeriv, knitr, rmarkdown, markdown,
Ryacas, Deriv, microbenchmark, MASS, ggplot2, nlraa

Performs univariate or multivariate computation of a single ODE or of a set of ODE (ordinary differential equations).

An R Package for Fitting Separable Nonlinear Models
Mariano Rodriguez-Arias <arias@unex.es>, Juan Antonio Fernandez <jfernandck@alumnos.unex.es>, Javier Cabello <coco@unex.es>, Rafael Benitez <rabesua@uv.es>
Set of functions implementing the algorithm described in Fernandez
Torvisco et al. (2018) for fitting separable nonlinear regression curves.
See Fernandez Torvisco, Rodriguez-Arias Fernandez and Cabello Sanchez (2018)
<doi:10.2298/FIL1812233T>.

Non-Linear Model Fitting of Time Distribution of Biological
Phenomena
c(
person("Nathan", "Eastwood", , "nathan.eastwood@icloud.com",
role = c("aut", "cre")),
person("Miguel", "Franco", , "M.Franco@plymouth.ac.uk", role = "ctb"),
person("Paul", "Ramsay", , "paul.ramsay@plymouth.ac.uk", role = "ctb"),
person("Nicola", "Steer", , "nicola.steer@plymouth.ac.uk", role = "ctb")
)
Fit biologically meaningful distribution functions to
time-sequence data (phenology), estimate parameters to draw the cumulative
distribution function and probability density function and calculate standard
statistical moments and percentiles.
Nathan Eastwood [aut, cre],
Miguel Franco [ctb],
Paul Ramsay [ctb],
Nicola Steer [ctb]