Hidden Markov Model for Financial Time-Series Based on Lambda Distribution R package ldhmm version 0.6.1 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 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", by Zucchini, MacDonald, Langrock (2016). Software Depends: R (>= 4.2.0) Imports: stats, utils, gnorm, optimx, xts (>= 0.10-0), zoo, moments, parallel, graphics, scales, ggplot2, grid, yaml, methods Suggests: knitr, testthat, depmixS4, roxygen2, R.rsp, shape Stephen H-T. Lihn <stevelihn@gmail.com> Comprehensive R Archive Network (CRAN) Stephen H-T. Lihn [aut, cre] Artistic-2.0 2023-12-11 application/tgz https://CRAN.R-project.org/package=ldhmm doi:10.32614/CRAN.package.ldhmm