Other packages > Find by keyword >

PRIMAL  

Parametric Simplex Method for Sparse Learning
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("PRIMAL", "1.0.3")



Attach the package and use:
library("PRIMAL")
Maintained by
Zichong Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-02
Latest Update: 2020-01-22
Description:
Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) .
How to cite:
Zichong Li (2019). PRIMAL: Parametric Simplex Method for Sparse Learning. R package version 1.0.3, https://cran.r-project.org/web/packages/PRIMAL. Accessed 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 08:16), 1.0.0 (2019-10-02 12:00), 1.0.1 (2020-01-12 15:40), 1.0.2 (2020-01-22 12:10)
Other packages that cited PRIMAL R package
View PRIMAL citation profile
Other R packages that PRIMAL depends, imports, suggests or enhances
Complete documentation for PRIMAL
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
dhReg  
Dynamic Harmonic Regression
Building and forecasting time series data with multiple seasonality using Dynamic Harmonic Regressio ...
Download / Learn more Package Citations See dependency  
noisyr  
Noise Quantification in High Throughput Sequencing Output
Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are use ...
Download / Learn more Package Citations See dependency  
dcov  
A Fast Implementation of Distance Covariance
Efficient methods for computing distance covariance and relevant statistics. See Sz ...
Download / Learn more Package Citations See dependency  
kernelPSI  
Post-Selection Inference for Nonlinear Variable Selection
Different post-selection inference strategies for kernelselection as described in kernelPSI a Post-S ...
Download / Learn more Package Citations See dependency  

28,083

R Packages

239,283

Dependencies

74,457

Author Associations

28,084

Publication Badges

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