Title: Classes and Methods for Seismic Data Analysis
Description: 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 <http://github.com/obsypy/obspy>. Additional classes and
methods support data returned by web services provided by the 'IRIS

methods support data returned by web services provided by the 'IRIS DMC'

Imports: methods, pracma, RCurl, seismicRoll (>= 1.1.0), signal,
the 'ObsPy' python toolbox <https://github.com/obsypy/obspy>. Additional classes and

the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and
Packaged: 2020-10-01 17:39:38 UTC; gilliansharer

Packaged: 2021-03-22 16:24:17 UTC; gilliansharer

Packaged: 2021-10-07 01:01:53 UTC; gilliansharer

Packaged: 2022-03-29 15:50:02 UTC; gilliansharer

Packaged: 2022-04-07 00:35:12 UTC; gilliansharer

Packaged: 2022-10-14 19:36:37 UTC; gilliansharer

Title: Imputation Regularized Optimization Algorithm
Description: 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.

Description: 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.