| Name : R-fdapace
| |
| Version : 0.6.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp155.1.3
| Date : 2024-09-10 06:25:35
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| Group : Development/Libraries/Other
| Source RPM : R-fdapace-0.6.0-lp155.1.3.src.rpm
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| Size : 2.14 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Functional Data Analysis and Empirical Dynamics
|
Description :
A versatile package that provides implementation of various methods of
Functional Data Analysis (FDA) and Empirical Dynamics. The core of this
package is Functional Principal Component Analysis (FPCA), a key
technique for functional data analysis, for sparsely or densely sampled
random trajectories and time courses, via the Principal Analysis by
Conditional Estimation (PACE) algorithm. This core algorithm yields
covariance and mean functions, eigenfunctions and principal component
(scores), for both functional data and derivatives, for both dense
(functional) and sparse (longitudinal) sampling designs. For sparse
designs, it provides fitted continuous trajectories with confidence
bands, even for subjects with very few longitudinal observations. PACE
is a viable and flexible alternative to random effects modeling of
longitudinal data. There is also a Matlab version (PACE) that contains
some methods not available on fdapace and vice versa. Updates to
fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and
DMS-2014626. Please cite our package if you use it (You may run the
command citation(\"fdapace\") to get the citation format and bibtex
entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016)
< doi:10.1146/annurev-statistics-041715-033624> Chen, K., Zhang, X.,
Petersen, A., Müller, H.G. (2017) < doi:10.1007/s12561-015-9137-5>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
