(Helske 2017) <doi:10.18637/jss.v078.i10> includes fast functions for
Kalman filtering, smoothing, forecasting, and simulation of multivariate
exponential family state space models, with observations from Gaussian,
Poisson, binomial, negative binomial, and gamma distributions.

Suggests: knitr, lme4, MASS, Matrix, testthat
Description: State space modelling is an efficient and flexible framework for
includes computationally efficient functions for Kalman filtering, smoothing,
forecasting, and simulation of multivariate exponential family state space models,
with observations from Gaussian, Poisson, binomial, negative binomial, and gamma
distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.

Author: Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>)

Title: Kalman Filter, Smoother and Disturbance Smoother
Description: Naive implementation of the Kalman filter, smoother and disturbance
smoother for state space models. An interface to other implementations of the
filter that are available in other packages is also provided.
Suggests: dlm, dse, FKF, numDeriv, stsm.class
Packaged: 2014-01-25 23:26:13 UTC; javlacalle

Packaged: 2014-01-26 09:01:25 UTC; javlacalle

Packaged: 2014-01-26 19:55:17 UTC; javlacalle

Packaged: 2014-06-15 12:18:11 UTC; javlacalle

Packaged: 2014-07-26 13:08:33 UTC; javlacalle

Suggests: dlm, dse, FKF, KFAS, numDeriv, stsm
Packaged: 2015-01-28 19:23:02 UTC; javlacalle

Title: Kendall Functional Principal Component Analysis
Description: Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA(). Moreover, least square estimates of functional principal component scores are also provided. Refer to <arXiv:2102.00911>.
Imports: kader, utils, pracma, fdapace, fda, stats, graphics

Authors@R: c(person(given = "Rou", family = "Zhong", role = c("aut", "cre"),
person(given = "Jingxiao", family = "Zhang", role = "aut"))
Description: Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.