time series using the autocorrelation function and a partial autocorrelation
function for functional time series proposed in
Imports: CompQuadForm, pracma, fda, vars

Title: Functional data analysis in a mixed model framework
Description: Likelihood based analysis of 1-dimension functional data
in a mixed-effets model framework. Matrix computation are
approximated by semi-explicit operator equivalents with linear
Depends: Rcpp (>= 0.9.0), RcppArmadillo (>= 0.2.10), Formula (>= 1.0-0)
Packaged: 2011-08-01 09:48:14 UTC; Bo Markussen

in a mixed-effects model framework. Matrix computation are

LinkingTo: Rcpp (>= 0.9.0), RcppArmadillo (>= 0.2.10)

Title: Functional Data Analysis in a Mixed Model Framework

Authors@R: c(person("Bo", "Markussen", role = c("aut", "cre"),
computational complexity. Markussen (2013) <doi:10.3150/11-BEJ389>.

Maintainer: Natalya Pya Arnqvist <nat.pya@gmail.com>
Title: Model-Based Clustering for Functional Data with Covariates
Description: Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) <arXiv:1904.10265>. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.
Imports: stats, graphics, Matrix, parallel, foreach, doParallel,

Title: Regression with Partial Differential Regularizations, using the
Authors@R: c(person("Eardi", "Lila", role = c("aut", "cre"),
person("Laura M.", "Sangalli", role = "aut",
person("Luca", "Formaggia", role = "aut",
Depends: R (>= 3.0.0), stats, grDevices, graphics, rgl
Description: An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization.
Copyright: See the individual source files for copyrights information