Title: Fitting Nonlinear Models with Scale Mixture of Skew-Normal
Description: Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression, details in Garay, Lachos and Abanto-Valle (2011) <doi.org/10.1016/j.jkss.2010.08.003> and Lachos, Bandyopadhyay and Garay (2011) <doi.org/10.1016/j.spl.2011.03.019>.

Description: Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression, details in Garay, Lachos and Abanto-Valle (2011) <doi:10.1016/j.jkss.2010.08.003> and Lachos, Bandyopadhyay and Garay (2011) <doi:10.1016/j.spl.2011.03.019>.

Description: 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.
Suggests: minpack.lm, optimr, Rvmmin, Rcgmin, numDeriv, knitr,

residual problems. Its Gauss-Newton method frequently stops with 'singular

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

Suggests: minpack.lm, optimx, Rvmmin, Rcgmin, numDeriv, knitr,

Suggests: minpack.lm, optimx, numDeriv, knitr, rmarkdown, markdown,

Title: Functions for Nonlinear Least Squares Solutions - Updated 2022
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.
Ryacas, Deriv, microbenchmark, MASS, ggplot2, nlraa