Package: ldhmm
Type: Package
Title: Hidden Markov Model for Financial Time-Series Based on Lambda
        Distribution
Version: 0.4.5
Date: 2018-02-28
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://ssrn.com/abstract=2979516
Depends: R (>= 3.3.0)
Imports: stats, utils, ecd, optimx, xts (>= 0.10-0), zoo, moments,
        parallel, graphics, scales, ggplot2, grid, methods
Suggests: knitr, testthat, depmixS4, roxygen2, R.rsp, shape
VignetteBuilder: R.rsp
License: Artistic-2.0
Encoding: latin1
LazyData: true
RoxygenNote: 6.0.1
Collate: 'ldhmm-calc_stats_from_obs.R' 'ldhmm-numericOrNull-class.R'
        'ldhmm-package.R' 'ldhmm-class.R' 'ldhmm-conditional_prob.R'
        'ldhmm-constructor.R' 'ldhmm-decode_stats_history.R'
        'ldhmm-decoding.R' 'ldhmm-forecast_prob.R'
        'ldhmm-forecast_state.R' 'ldhmm-forecast_volatility.R'
        'ldhmm-fred_data.R' 'ldhmm-gamma_init.R' 'ldhmm-ld_stats.R'
        'ldhmm-log_forward.R' 'ldhmm-mle.R' 'ldhmm-mllk.R'
        'ldhmm-n2w.R' 'ldhmm-oxford_man_index_list.R'
        'ldhmm-oxford_man_plot_obs.R'
        'ldhmm-oxford_man_realized_data.R' 'ldhmm-oxford_man_ts.R'
        'ldhmm-plot_spx_vix_obs.R' 'ldhmm-pseudo_residuals.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'
        'sldhmm-class.R'
NeedsCompilation: no
Packaged: 2018-02-28 14:25:12 UTC; slihn
Repository: CRAN
Date/Publication: 2018-02-28 14:36:06 UTC
Built: R 3.4.4; ; 2019-04-25 21:42:37 UTC; windows
