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. See Sangalli, L. M. (2021). Spatial Regression With Partial Differential Equation Regularisation. International Statistical Review, 89(3), 505-531. for an overview.

Title: Partially Observed Integrated Functional Depth
Authors@R: c(person("Antonio", "Elías", email = "antonioefz91@gmail.com", role = c("aut", "cre")),
person("Raúl", "Jiménez", email = "", role = c("ctb")),
person("Anna M.", "Paganoni", email = "", role = c("ctb")),
person("Laura M.", "Sangalli", email = "", role = c("ctb")))
Description: Integrated Depths for Partially Observed Functional Data (PoFD). Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2020), "Integrated Depth for Partially Observed Functional Data".
Imports: ggplot2, tibble, magrittr, reshape2, patchwork, MASS, fdapace,

Description: Integrated Depths for Partially Observed Functional Data (POFD). Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2020), "Integrated Depth for Partially Observed Functional Data".

Description: Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2022), "Integrated Depth for Partially Observed Functional Data". Journal of Computational and Graphical Statistics. <doi:10.1080/10618600.2022.2070171>.

Title: Joint Clustering and Alignment of Functional Data
Description: Revisited clustering approaches to accommodate functional
data by allowing to jointly align the data during the clustering
process. Currently, shift, dilation and affine transformations
only are available to perform alignment. The k-mean algorithm has
been extended to integrate alignment and is fully parallelized.
Hierarchical clustering will soon be available as well.
Imports: Rcpp, magrittr, tibble, dplyr, tidyr, purrr, ggplot2, nloptr