Title: The Sparse Online Principal Component Estimation Algorithm
Description: The sparse online principal component can not only process the real-time updated data set and stream data set, but also obtain the sparse solution of the updated data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>.
Packaged: 2021-09-17 01:39:49 UTC; Administrator

Packaged: 2022-04-04 05:04:37 UTC; Administrator

Authors@R: c(person("Guangbao", "Guo", role = c("aut", "cre"), email = "ggb11111111@163.com",comment =  c(ORCID = " ")), person("Chunjie", "Wei", role="aut"), person("Guoqi", "Qian", role="aut"))
Description: The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>.
Packaged: 2022-04-16 12:15:30 UTC; Administrator

Description: The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

Title: Non-Parametric Estimation of the Off-Pulse Interval of a Pulsar
Maintainer: Willem Daniel Schutte <wd.schutte@nwu.ac.za>
Description: Provides functions to non-parametrically estimate the off-pulse interval of a source
function originating from a pulsar. The technique is based on a sequential application of P-values
obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.

Depends: R (>= 3.2), circular, ADGofTest

Depends: R (>= 4.0), circular, ADGofTest, grDevices, graphics

Title: Estimation using Sequential Offsetted Regression
Author: Lee S. McDaniel, Jonathan S. Schildcrout
Maintainer: Lee S. McDaniel <lmcda4@lsuhsc.edu>
Description: Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.