Package: ldhmm
Type: Package
Title: Hidden Markov Model for Financial Time-Series Based on Lambda
        Distribution
Version: 0.6.1
Date: 2023-12-31
Authors@R: person(given = c("Stephen", "H-T."), family = "Lihn",
                  email = "stevelihn@gmail.com", role = c("aut", "cre"))
Author: Stephen H-T. Lihn [aut, cre]
Maintainer: Stephen H-T. Lihn <stevelihn@gmail.com>
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
        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).
URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2979516
        https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3435667
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
License: Artistic-2.0
Encoding: UTF-8
RoxygenNote: 7.2.3
Collate: 'ecld-cdf-method.R' 'ecld-class.R' 'ecld-constructor.R'
        'ecld-pdf-method.R' 'ecld-sd-method.R'
        'ldhmm-calc_stats_from_obs.R' 'ldhmm-numericOrNull-class.R'
        'ldhmm-package.R' 'ldhmm-class.R' 'ldhmm-conditional_prob.R'
        'ldhmm-constructor.R' 'ldhmm-data-config-internal.R'
        'ldhmm-decode_stats_history.R' 'ldhmm-decoding.R'
        'ldhmm-df2ts-method.R' 'ldhmm-forecast_prob.R'
        'ldhmm-forecast_state.R' 'ldhmm-forecast_volatility.R'
        'ldhmm-fred_data.R' 'ldhmm-gamma_init.R'
        'ldhmm-get-data-method.R' 'ldhmm-ld_stats.R'
        'ldhmm-log_forward.R' 'ldhmm-mle.R' 'ldhmm-mllk.R'
        'ldhmm-n2w.R' 'ldhmm-plot_spx_vix_obs.R'
        'ldhmm-pseudo_residuals.R' 'ldhmm-read-csv-by-symbol-method.R'
        'ldhmm-read_sample_object.R' 'ldhmm-simulate_abs_acf.R'
        'ldhmm-simulate_state_transition.R' 'ldhmm-sma.R'
        'ldhmm-state_ld.R' 'ldhmm-state_pdf.R' 'ldhmm-ts_abs_acf.R'
        'ldhmm-ts_log_rtn.R' 'ldhmm-viterbi.R' 'ldhmm-w2n.R'
NeedsCompilation: no
Packaged: 2026-10-11 08:43:23 UTC; root
Config/pak/sysreqs: cmake
Repository: https://slihn.r-universe.dev
Date/Publication: 2023-12-12 02:42:22 UTC
RemoteUrl: https://github.com/cran/ldhmm
RemoteRef: HEAD
RemoteSha: a013108005fb4a75d86a8e1a86a276060cb11db8