Title: Longitudinal Drift-Diffusion Mixed Models (LDDMM)
Authors@R: c(person("Giorgio", "Paulon", email = "giorgio.paulon@utexas.edu",
person("Abhra", "Sarkar", email = "abhra.sarkar@utexas.edu",
Description: Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021).
Imports: Rcpp (>= 1.0.6), RcppProgress, rgen, gtools, LaplacesDemon,
dplyr, plyr, tidyr, ggplot2, latex2exp, reshape2, RColorBrewer
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, rgen

Title: Hidden Markov Model for Return Time-Series Based on Lambda
Description: Hidden Markov Model (HMM) based on symmetric lambda distribution
framework is implemented for the study of return time-series in the financial
market. Major features in the S&P500 index, such as regime identification,
volatility clustering, and anti-correlation between return and volatility,
can be extracted from HMM cleanly. Univariate symmetric lambda distribution
is essentially a location-scale family of power-exponential distribution. Such
distribution is suitable for describing highly leptokurtic time series obtained
from the financial market. It provides a theoretically solid foundation to
explore such data where the normal distribution is not adequate.
The HMM implementation follows closely the book: "Hidden Markov Models for Time Series",
by Zucchini, MacDonald, Langrock (2016).
Imports: stats, utils, ecd, xts, zoo, moments, parallel, graphics,
Suggests: knitr, testthat, depmixS4, roxygen2, scales, shape

Title: Hidden Markov Model for Financial Time-Series Based on Lambda
is essentially a location-scale family of exponential power distribution.
Such distribution is suitable for describing highly leptokurtic time series
obtained from the financial market. It provides a theoretically solid foundation
to explore such data where the normal distribution is not adequate. The HMM
implementation follows closely the book: "Hidden Markov Models for Time Series",
Imports: stats, utils, ecd, optimx, xts, zoo, moments, parallel,
graphics, scales, ggplot2, grid, methods
Suggests: knitr, testthat, depmixS4, roxygen2, R.rsp, shape