The Sparse Online Principal Component Estimation Algorithm
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"))
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>.
Guangbao Guo [aut, cre],
Chunjie Wei [aut],
Guoqi Qian [aut]

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"))
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>.

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>.

Non-Parametric Estimation of the Off-Pulse Interval of a Pulsar
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.

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

Estimation using Sequential Offsetted Regression
Lee S. McDaniel, Jonathan S. Schildcrout
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.

c(person("Lee", "McDaniel", role = c("aut", "cre"),email = "lmcda4@lsuhsc.edu"),
person("Jonathan", "Schildcrout", role = "aut"))
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.  The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates.  The second regression uses this estimated probability to calculate the mean population response given covariates.
Lee McDaniel [aut, cre],
Jonathan Schildcrout [aut]