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gettingtothebottom (version 3.0)

Getting to the Bottom, A Package for Learning Optimization Methods

Description

Getting to the Bottom is a companion package for the "Getting to the Bottom" optimization methods series at Statisticsviews.com. The package contains data and code to reproduce the examples in the articles.

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Version

Install

install.packages('gettingtothebottom')

Monthly Downloads

35

Version

3.0

License

CC BY-NC-SA 4.0

Maintainer

Jocelyn Chi

Last Published

August 2nd, 2014

Functions in gettingtothebottom (3.0)

plot_solutionpaths

MM Algorithm - Plot results of solutionpaths function
plot_loss

Gradient Descent Algorithm - Plotting the Loss Function
baltimoreyouth

Baltimore Youth Indicators - 2010 and 2011
makeZ

MM Algorithm - Make Z
testmatrix

MM Algorithm - Generate Test Matrix
init.lambda

MM Algorithm - Initial lambda
plot_spect

MM Algorithm - Plotting the Spectroscopic Signal
make_noise

Linear Programming - Generate matrix of noisy data for sparse quantile regression
gdescent

Gradient Descent Algorithm
plot_nnm_truth

MM Algorithm - Plotting the True Signal
softhreshold

MM Algorithm - Softhreshold Function
plot_iterates

Gradient Descent Algorithm - Plotting the Iterates
plot_solpaths_error

MM Algorithm - Function for plotting the imputed values against the truth for minimum error solution
generate_sparse_data

Linear Programming - Generate simulated data for sparse quantile regression
moviebudgets

Movie ratings and budget database derived from data from IMDB.com
nutrition

The Diet Problem: "Daily Allowances of Nutrients for a Moderately Active Man (weighing 154 pounds)" from George Stigler's 1945 paper on "The Cost of Subsistence"
check_func

Linear Programming - Check function
generate_nnm

Generate random nonnegative mixture components
plot_nnm

MM Algorithm - Plot NNM
movieratings

Movie ratings database derived from data from IMDB.com
example.quadratic.approx

Gradient Descent Algorithm - Plots Depicting How Different Choices of Alpha Result in Differing Quadratic Approximations
plot_softhreshold

MM Algorithm - Plot the Softhreshold Function
makeLambdaseq

MM Algorithm - Function for making sequence of lambdas for solution paths
example.alpha

Gradient Descent Algorithm - Plots Depicting Gradient Descent Results in Example 1 Using Different Choices for the Step Size
generate_data

Linear Programming - Generate simulated data for LAD regression
nnls_mm

Nonnegative Least Squares via MM
diff_norm

MM Algorithm - Normed Difference
ladlp

Linear Programming - Least absolute deviations (LAD) regression
plot_gradient

Gradient Descent Algorithm - Plotting the Gradient Function
stigler

The Diet Problem: "Nutritive Values of Common Foods per Dollar of Expenditure, August 15, 1944", from George Stigler's 1945 paper on "The Cost of Subsistence"
plot_quantreg_noisy

Linear Programming - Function for plotting results of sparse quantile regression
plot_nnm_reconstruction

MM Algorithm - Plotting the Reconstruction
plot_nnm_coef

MM Algorithm - Plotting the NNMLS regression coefficients
plot_check

Linear Programming - Plot Check Function
makeY

MM Algorithm - Make Y
makeOmega

MM Algorithm - Generate Omega
solutionpaths

MM Algorithm - Find the best fit lambda for a given problem based on an initial guess for lambda
gettingtothebottom

gettingtothebottom
matrixcomplete

MM Algorithm - Matrix Completion
quantreg

Linear Programming - Quantile regression
quantreglp

Linear Programming - Linear programming solver for quantile regression
plot_nnm_obj

MM Algorithm - Plot NNM Objective
plot_quantreg

Linear Programming - Function for plotting results of bivariate quantile regression.
engel

Engel's Law - Engel Food Expenditures Data from the quantreg package for R