Other packages > Find by keyword >

GLDreg  

Fit GLD Regression/Quantile/AFT Model to Data
View on CRAN: Click here


Download and install GLDreg package within the R console
Install from CRAN:
install.packages("GLDreg")

Install from Github:
library("remotes")
install_github("cran/GLDreg")

Install by package version:
library("remotes")
install_version("GLDreg", "1.1.1")



Attach the package and use:
library("GLDreg")
Maintained by
Steve Su
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-11-11
Latest Update: 2022-05-13
Description:
Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model".
How to cite:
Steve Su (2014). GLDreg: Fit GLD Regression/Quantile/AFT Model to Data. R package version 1.1.1, https://cran.r-project.org/web/packages/GLDreg. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.0.1 (2014-12-09 08:43), 1.0.2 (2015-03-05 07:41), 1.0.3 (2015-07-04 15:33), 1.0.4 (2016-07-28 17:28), 1.0.5 (2016-12-26 12:25), 1.0.6 (2017-01-29 10:16), 1.0.7 (2017-02-28 10:58), 1.0 (2014-11-11 12:23), 1.1.0 (2022-05-13 09:30)
Other packages that cited GLDreg R package
View GLDreg citation profile
Other R packages that GLDreg depends, imports, suggests or enhances
Complete documentation for GLDreg
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

SCBiclust  
Identifies Mean, Variance, and Hierarchically Clustered Biclusters
Identifies a bicluster, a submatrix of the data such that the features and observations within the s ...
Download / Learn more Package Citations See dependency  
RcppHNSW  
'Rcpp' Bindings for 'hnswlib', a Library for Approximate Nearest Neighbors
'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R int ...
Download / Learn more Package Citations See dependency  
crossrun  
Joint Distribution of Number of Crossings and Longest Run
Joint distribution of number of crossings and the longest run in a series of independent Bernoulli ...
Download / Learn more Package Citations See dependency  
pkgdepends  
Package Dependency Resolution and Downloads
Find recursive dependencies of 'R' packages from various sources. Solve the dependencies to obtain ...
Download / Learn more Package Citations See dependency  
deductive  
Data Correction and Imputation Using Deductive Methods
Attempt to repair inconsistencies and missing values in data records by using information from vali ...
Download / Learn more Package Citations See dependency  
kgschart  
KGS Rank Graph Parser
Restore underlining numeric data from rating history graph of KGS (an online platform of the game o ...
Download / Learn more Package Citations See dependency  

23,229

R Packages

199,929

Dependencies

62,984

Author Associations

23,230

Publication Badges

© Copyright 2022 - present. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA