Title: Functional Data Analysis: K-Mean Alignment
Author: Alice Parodi, Mirco Patriarca, Laura Sangalli, Piercesare Secchi, Simone Vantini, Valeria Vitelli

Author: Mirco Patriarca, Laura Sangalli, Piercesare Secchi, Simone Vantini, Valeria Vitelli, Alice Parodi.

Description: It performs simultaneously clustering and alignment of a multidimensional or unidimensional functional dataset by means of k-mean alignment.

Description: It jointly performs clustering and alignment of a multidimensional or unidimensional functional dataset by means of k-mean alignment.

Title: Outlier Detection Tools for Functional Data Analysis
Description: A collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness, MS-plot, total variation depth, and sequential transformations among others.
Suggests: testthat (>= 2.1.0), covr, spelling
Packaged: 2020-11-24 14:46:41 UTC; statimatician
Author: Oluwasegun Taiwo Ojo [aut, cre, cph]
Maintainer: Oluwasegun Taiwo Ojo <seguntaiwoojo@gmail.com>

Packaged: 2020-11-27 16:57:05 UTC; statimatician

Description: A collection of functions for outlier detection in functional data analysis.
Methods implemented include directional outlyingness by
total variation depth and modified shape similarity index by
Huang and Sun (2019) <doi:10.1080/00401706.2019.1574241>, and sequential transformations by
Dai et al. (2020) <doi:10.1016/j.csda.2020.106960 among others. Additional outlier detection
tools and depths for functional data like functional boxplot, (modified) band depth etc.,
Packaged: 2021-03-02 12:36:54 UTC; statimatician

Title: Functional Data Analysis and Empirical Dynamics
Maintainer: Pantelis Z. Hadjipantelis <pantelis@ucdavis.edu>
Description: 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 or numerical integration. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) random trajectories. It does not rely on pre-smoothing of trajectories, which is problematic if functional data are sparsely sampled. PACE provides options for functional regression and correlation, for Longitudinal Data Analysis, the analysis of stochastic processes from samples of realized trajectories, and for the analysis of underlying dynamics. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
Imports: Rcpp (>= 0.11.5), Hmisc, plot3D, Matrix, pracma, numDeriv
Suggests: rgl, aplpack, mgcv, ks, MASS, gtools