| Name : R-nevada
| |
| Version : 0.2.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.5.2
| Date : 2024-09-12 10:44:33
|
| Group : Development/Libraries/Other
| Source RPM : R-nevada-0.2.0-lp153.5.2.src.rpm
|
| Size : 0.54 MB
| |
| Packager : (none)
| |
| Summary : Network-Valued Data Analysis
|
Description :
A flexible statistical framework for network-valued data analysis. It
leverages the complexity of the space of distributions on graphs by
using the permutation framework for inference as implemented in the
\'flipr\' package. Currently, only the two-sample testing problem is
covered and generalization to k samples and regression will be added in
the future as well. It is a 4-step procedure where the user chooses a
suitable representation of the networks, a suitable metric to embed the
representation into a metric space, one or more test statistics to
target specific aspects of the distributions to be compared and a
formula to compute the permutation p-value. Two types of inference are
provided: a global test answering whether there is a difference between
the distributions that generated the two samples and a local test for
localizing differences on the network structure. The latter is assumed
to be shared by all networks of both samples. References: Lovato, I.,
Pini, A., Stamm, A., Vantini, S. (2020) \"Model-free two-sample test for
network-valued data\" < doi:10.1016/j.csda.2019.106896> Lovato, I.,
Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) \"Multiscale null
hypothesis testing for network-valued data: Analysis of brain networks
of patients with autism\" < doi:10.1111/rssc.12463>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
