Systematically creates and modifies NONMEM(R) control streams. Harvests
 NONMEM output, builds run logs, creates derivative data, generates diagnostics.
 NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for 
 nonlinear mixed effects modeling. See 'package?nonmemica'.

Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0),
  using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in
  (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).

Testing non-nested models via theory supplied by Vuong (1989) <DOI:10.2307/1912557>.
    Includes tests of model distinguishability and of model fit that can be applied
    to both nested and non-nested models. Also includes functionality to obtain
    confidence intervals associated with AIC and BIC. This material is partially based on
    work supported by the National Science Foundation under Grant Number SES-1061334.

Implements calculation of probability density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for the following multivariate distributions: Lomax (Pareto Type II), generalized Lomax, Mardia’s Pareto of Type I, Logistic, Burr, Cook-Johnson’s uniform, F and Inverted Beta. See Tapan Nayak (1987) <doi:10.2307/3214068>.

Contains the following 5 nonparametric hypothesis tests:
    The Sign Test,
    The 2 Sample Median Test,
    Miller's Jackknife Procedure,
    Cochran's Q Test, &
    The Stuart-Maxwell Test.

Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.

Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.