fdaconcur: Concurrent Regression and History Index Models for Functional
Data
Provides implementation of concurrent or varying coefficient regression methods for
functional data. The implementations are done for both dense and sparsely observed functional
data. Pointwise confidence bands can be constructed for each case. Further, the influence of
past predictor values is modeled by a smooth history index function,
while the effects on the response are described by smooth varying coefficient functions,
which are very useful in analyzing real data such as COVID data.
References: Yao, F., Müller, H.G., Wang, J.L. (2005) <doi:10.1214/009053605000000660>.
Sentürk, D., Müller, H.G. (2010) <doi:10.1198/jasa.2010.tm09228>.
| Version: |
0.1.0 |
| Imports: |
fdapace, stats |
| Suggests: |
MASS, Rcpp (≥ 0.11.5), Matrix, pracma, numDeriv, testthat |
| Published: |
2022-09-20 |
| Author: |
Satarupa Bhattacharjee [aut, cre],
Yaqing Chen [aut],
Changbo Zhu [aut],
Han Chen [aut],
Yidong Zhou [aut],
Álvaro Gajardo [aut],
Hans-Georg Müller [cph, ths, aut] |
| Maintainer: |
Satarupa Bhattacharjee <sbhattacharjee at ucdavis.edu> |
| BugReports: |
https://github.com/functionaldata/tFDAconcur/issues |
| License: |
BSD_3_clause + file LICENSE |
| URL: |
https://github.com/functionaldata/tFDAconcur |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
fdaconcur results |
Documentation:
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