Description: A binding to the minimist JavaScript library. This module implements
the guts of optimist's argument parser without all the fanciful decoration.

person("Daniel", "Lemire", role = "cph"),
person("Chad", "Walters", role = "cph"))
Description: Self-sufficient reader for a subset of Parquet files. Nested tables, compression besides Snappy and encryption are not supported.

Title: R Interface to the Levenberg-Marquardt Nonlinear Least-Squares
Algorithm Found in MINPACK, Plus Support for Bounds
Author: Timur V. Elzhov, Katharine M. Mullen, Andrej-Nikolai Spiess, Ben Bolker
Maintainer: Katharine M. Mullen <mullenkate@gmail.com>
Description: The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.

Title: Derivative-free optimization algorithms by quadratic
Author: Douglas Bates, Katharine M. Mullen, John C. Nash, Ravi Varadhan
Description: Derivative-free optimization by quadratic approximation
based on an interface to Fortran implementations by M. J. D.

Author: Katharine M. Mullen, John C. Nash, Ravi Varadhan

Title: Derivative-Free Optimization Algorithms by Quadratic

Title: An R Package for Linear Mixed Model Analyses
Description: This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.

Title: Various Linear Mixed Model Analyses

Description: Using this package, one can determine the minimum sample size required so that the absolute deviation of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.