Description: Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits.
datasets, globals (>= 0.12), knitr (>= 1.28), rmarkdown (>=
Collate: 'allocate_wave.R' 'sample_strata.R' 'wave.R' 'phase.R'

Title: R port of the Scilab optimbase module
Description: Provides a set of commands to manage an abstract
optimization method. The goal is to provide a building block
for a large class of specialized optimization methods. This
package manages: the number of variables, the minimum and
maximum bounds, the number of non linear inequality
constraints, the cost function, the logging system, various
Packaged: 2010-05-05 11:13:48 UTC; sebastien

Title: R Port of the 'Scilab' Optimbase Module
Suggests: knitr (>= 1.28), rmarkdown (>= 2.2)
Packaged: 2022-01-24 22:03:46 UTC; sebastien

Packaged: 2010-05-12 01:24:54 UTC; sebastien

Packaged: 2010-05-26 01:48:58 UTC; sebastien

Packaged: 2011-01-16 02:42:31 UTC; sebastien

Packaged: 2011-03-16 23:08:21 UTC; sebastien

Packaged: 2014-02-01 13:14:04 UTC; sebastien

Packaged: 2014-03-02 03:29:40 UTC; sebastien

Packaged: 2010-05-05 04:43:28 UTC; sebastien

Author: Vithor Rosa Franco <vithorfranco@gmail.com>
Maintainer: Vithor Rosa Franco <vithorfranco@gmail.com>
Description: Provides general purpose tools for helping users to implement steepest
gradient descent methods for function optimization; for details see
Ruder (2016) <arXiv:1609.04747v2>. Currently, the Steepest 2-Groups
Gradient Descent and the Adaptive Moment Estimation (Adam) are the
methods implemented. Other methods will be implemented in the future.

Description: Flexible multi-purpose optimizer with numerous input specifications. It allows a very detailed parameterization and is therefore useful for specific and complex loss functions, like functions with discrete parameter space. Visualization tools for validation and analysis of the convergence are also included.