Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye 
  movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at 
  The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal 
  Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. 
  There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and 
  lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing 
  within moments. The other three functions are supportive functions which visualize the eye 
  fixation patterns.

Computes the power and sample size (PASS) required to test for the
    difference in the mean function between two groups under a repeatedly measured longitudinal 
    or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> 
    for details of the PASS formula and computational details. The details of the testing
    procedure for univariate and multivariate response are presented in
    Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) 
    <arXiv:2302.05612> respectively.

Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Standardisation of cluster validation statistics by random clusterings and 
  comparison between many clustering methods and numbers of clusters based on
  this.  
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.