c(
person("Jonathan", "Callahan", email="jonathan@mazamascience.com", role=c("aut")),
person("Rob", "Casey", email="rob@iris.washington.edu", role=c("aut")),
person("Gillian", "Sharer", email="gillian@iris.washington.edu", role=c("aut","cre")),
person("Mary", "Templeton", email="met@iris.washington.edu", role=c("aut")),
person("Chad", "Trabant", email="chad@iris.washington.edu", role=c("ctb"))
)
Jonathan Callahan [aut],
Rob Casey [aut],
Gillian Sharer [aut, cre],
Mary Templeton [aut],
Chad Trabant [ctb]

Provides classes and methods for seismic data analysis. The
base classes and methods are inspired by the python code found in
the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and
methods support data returned by web services provided by the 'IRIS DMC'
<http://service.iris.edu/>.

c(person("Bochao", "Jia", role = c("aut", "cre", "cph"), email = "jbc409@ufl.edu"),
person("Faming", "Liang", role = c("ctb"), email = "fmliang@purdue.edu"))
Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at <https://people.clas.ufl.edu/yeluo/files/ica10.pdf> for detail. The publication "An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond" will be appear on Journal of the Royal Statistical Society Series B soon.
Bochao Jia [aut, cre, cph],
Faming Liang [ctb]

Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at <arXiv:1802.02251> for detail.