Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.

A collection of methods to estimate parameters of different tempered stable 
  distributions (TSD). Currently, there are seven different tempered stable 
  distributions to choose from: Tempered stable subordinator distribution, 
  classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid 
  decreasing TSD, and Kim-Rachev TSD.
  The package also provides functions to compute density and probability 
  functions and tools to run Monte Carlo simulations.
  This package has already been used for the estimation of tempered stable 
  distributions (Massing (2023) <arXiv:2303.07060>).
  The following references form the theoretical background for various functions
  in this package. References for each function are explicitly listed in its 
  documentation:
  Bianchi et al. (2010) <doi:10.1007/978-88-470-1481-7_4>
  Bianchi et al. (2011) <doi:10.1137/S0040585X97984632>
  Carrasco (2017) <doi:10.1017/S0266466616000025>
  Feuerverger (1981) <doi:10.1111/j.2517-6161.1981.tb01143.x>
  Hansen et al. (1996) <doi:10.1080/07350015.1996.10524656>
  Hansen (1982) <doi:10.2307/1912775>
  Hofert (2011) <doi:10.1145/2043635.2043638>
  Kawai & Masuda (2011) <doi:10.1016/j.cam.2010.12.014>
  Kim et al. (2008) <doi:10.1016/j.jbankfin.2007.11.004>
  Kim et al. (2009) <doi:10.1007/978-3-7908-2050-8_5>
  Kim et al. (2010) <doi:10.1016/j.jbankfin.2010.01.015>
  Kuechler & Tappe (2013) <doi:10.1016/j.spa.2013.06.012>
  Rachev et al. (2011) <doi:10.1002/9781118268070>.

TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed.