A collection of functions for outlier detection in functional data analysis.
Methods implemented include directional outlyingness by
Dai and Genton (2019) <doi:10.1016/j.csda.2018.03.017>,
MS-plot by Dai and Genton (2018) <doi:10.1080/10618600.2018.1473781>,
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.,
are also available.

Functional Data Analysis and Empirical Dynamics
Xiongtao Dai,
Pantelis Z. Hadjipantelis,
Hao Ji,
Hans-Georg Mueller,
Jane-Ling Wang
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".
Rcpp (>= 0.11.5), Hmisc, plot3D, Matrix, pracma, numDeriv

Rcpp (>= 0.11.5), Hmisc, Matrix, pracma, numDeriv
plot3D, rgl, aplpack, mgcv, ks, MASS, gtools, knitr, Rmixmod,
minqa, testthat

c(person("Xiongtao","Dai", role=c("aut")),
person("Pantelis Z.","Hadjipantelis", email="pantelis@ucdavis.edu", role=c("aut","cre")),
person("Kynghee","Han", role="aut"),
person("Hao","Ji", role=c("aut")),
person("Shu-Chin", "Lin", role="ctb"),
person("Hans-Georg", "Mueller", role=c("cph","ths")),
person("Jane-Ling", "Wang", role=c("cph","ths")))
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".
Rcpp (>= 0.11.5), Hmisc, MASS, Matrix, pracma, numDeriv
plot3D, rgl, aplpack, mgcv, ks, gtools, knitr, EMCluster,
minqa, testthat
Xiongtao Dai [aut],
Pantelis Z. Hadjipantelis [aut, cre],
Kynghee Han [aut],
Hao Ji [aut],
Shu-Chin Lin [ctb],
Hans-Georg Mueller [cph, ths],
Jane-Ling Wang [cph, ths]