R interface to 'PRIMME' <http://www.cs.wm.edu/~andreas/software>, a C library for computing a few
Hermitian matrix, or generalized Hermitian eigenproblem.  It can also compute
singular values and vectors of a square or rectangular matrix. 'PRIMME' finds
largest, smallest, or interior singular/eigenvalues and can use preconditioning
to accelerate convergence. General description of the methods are provided in the papers

R interface to 'PRIMME' <http://www.cs.wm.edu/~andreas/software/>, a C library for computing a few

R interface to 'PRIMME' <https://www.cs.wm.edu/~andreas/software/>, a C library for computing a few

Title: PRIM Survival Regression Classification
Author: Jean-Eudes Dazard [aut, cre], Michael Choe [ctb], Michael LeBlanc [ctb], Alberto Santana [ctb]
Description: Performs a unified treatment of Bump Hunting by Patient Rule Induction Method (PRIM) in Survival, Regression and Classification settings (SRC). The current version is a development release that only implements the case of a survival response. New features will be added soon as they are available.
Depends: R (>= 3.0.2), parallel, survival, Hmisc, glmnet, MASS

Depends: R (>= 3.0.2), survival, glmnet, superpc, Hmisc, quantreg

Description: Performs a unified treatment of Bump Hunting by Patient Rule Induction Method (PRIM) in Survival, Regression and Classification settings (SRC). The current version is a development release that only implements the case of a survival response.

Title: Penalized Regression with Inferred Seasonality Module -
Forecasting Unemployment Initial Claims using 'Google Trends'
person("Dingdong", "Yi", email = "ddyi@fas.harvard.edu", role = c("aut", "cre")),
person("Samuel", "Kou", email = "kou@stat.harvard.edu", role = c("aut")))
Description: Implements Penalized Regression with Inferred Seasonality Module (PRISM) to generate forecast estimation of weekly unemployment initial claims using 'Google Trends' data. It includes required data and tools for backtesting the performance in 2007-2016.

person("Dingdong", "Yi", email = "ryan.ddyi@gmail.com", role = c("aut", "cre")),
person("Samuel", "Kou", email = "kou@stat.harvard.edu", role = c("aut")),
person("Shaoyang", "Ning", email = "sn9@williams.edu", role = c("aut")))
Description: Implements Penalized Regression with Inferred Seasonality Module (PRISM) to generate forecast estimation of weekly unemployment initial claims using 'Google Trends' data. It includes required data and tools for backtesting the performance in 2007-2020.