R: Hidden Markov Model for Financial Time-Series Based on Lambda Distribution

# Hidden Markov Model for Financial Time-Series Based on Lambda Distribution

## Documentation for package ‘ldhmm’ version 0.6.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

ldhmm-package | ldhmm: A package for HMM using lambda distribution.
ecld | Constructor of ecld class
ecld-class | An S4 class to represent the lambda distribution
ecld.ccdf | CDF and CCDF of ecld
ecld.cdf | CDF and CCDF of ecld
ecld.kurt | Compute statistics analytically for an ecld object
ecld.kurtosis | Compute statistics analytically for an ecld object
ecld.mean | Compute statistics analytically for an ecld object
ecld.pdf | Calculate the PDF of an ecld object
ecld.sd | Compute statistics analytically for an ecld object
ecld.skewness | Compute statistics analytically for an ecld object
ecld.var | Compute statistics analytically for an ecld object
ldhmm | Constructor of ldhmm class
ldhmm-class | The ldhmm class
ldhmm.calc_stats_from_obs | Computing the statistics for each state
ldhmm.conditional_prob | Computing the conditional probabilities
ldhmm.decode_stats_history | Estimating historical statistics (mean, volatility and kurtosis)
ldhmm.decoding | Computing the minus log-likelihood (MLLK)
ldhmm.df2ts | Utility to standardize timeseries from data.frame to xts
ldhmm.drop_outliers | Computing the statistics for each state
ldhmm.forecast_prob | Computing the forecast probability distribution
ldhmm.forecast_state | Computing the state forecast
ldhmm.forecast_volatility | Computing the volatility forecast for next one period
ldhmm.fred_data | Utility to download time series from FRED
ldhmm.gamma_init | Initializing tansition probability paramter
ldhmm.get_data | Read sample data
ldhmm.get_data.arr | Read sample data
ldhmm.get_data.ts | Read sample data
ldhmm.ld_stats | Computes the theoretical statistics per state
ldhmm.log_backward | Computing the log forward and backward probabilities
ldhmm.log_forward | Computing the log forward and backward probabilities
ldhmm.mle | Computing the MLEs
ldhmm.mllk | Computing the minus log-likelihood (MLLK)
ldhmm.n2w | Transforming natural parameters to a linear working parameter array
ldhmm.plot_spx_vix_obs | Plotting HMM expected volatility for SPX overlaid with adjusted VIX
ldhmm.pseudo_residuals | Computing pseudo-residuals
ldhmm.read_csv_by_symbol | Read csv file of sample data
ldhmm.read_sample_object | Read sample ldhmm object
ldhmm.simulate_abs_acf | Simulating auto-correlation (ACF)
ldhmm.simulate_state_transition | Simulating state transition
ldhmm.sma | Simple moving average of a time series
ldhmm.state_ld | Constructing the ecld objects per state
ldhmm.state_pdf | Computing the PDF per state given the observations
ldhmm.ts_abs_acf | Computing ACF of the absolute value of a time series
ldhmm.ts_log_rtn | Get log-returns from historic prices of an index
ldhmm.viterbi | Computing the global decoding by the Viterbi algorithm
ldhmm.w2n | Transforming working parameter array to natural parameters
numericOrNull-class | The numericOrNull class