fHMM 1.4.1

fHMM 1.4.0

Improved initialization of the numerical likelihood optimization.
Now the states after model estimation are automatically ordered according to the estimated mean of the state-dependent distributions, see reorder_states() with the new (default) option state_order = "mean".
Re-fitted the example models contained in the package.

Controls can now be provided separately for the set_controls() function.
The arguments in fHMM_parameters() for model parameters were slightly renamed as follows:
- mus -> mu
- sigmas -> sigma
- dfs -> df
- Gammas_star -> Gamma_star
- mus_star -> mu_star
- sigmas_star -> sigma_star
- dfs_star -> df_star
The log-normal state-dependent distribution is renamed: lnorm -> lognormal.
Two more state-dependent distributions were added: normal and poisson.
The Viterbi algorithm can be directly accessed via viterbi().
Renamed simulate_data() -> simulate_hmm() to make the functionality clearer. Furthermore, this function is now exported and can be used outside of the package to simulate HMM data.
download_data() no longer saves a .csv-file but returns the data as a data.frame. Its verbose argument is removed because the function no longer prints any messages.
The utilities (i.e., all functions with roxygen tag @keywords utils) were moved to the {oeli} package.

Fixed documenting the new special sentinel “_PACKAGE” for the package help file, see https://github.com/r-lib/roxygen2/issues/1491.

Extended the time horizon of saved data and updated models for demonstration.
The download_data() function now returns the data as a data.frame by default. However, specifying argument file still allows for saving the data as a .csv file.
The plot.fHMM_model() function now has the additional argument ll_relative (default is TRUE) to plot the relative log-likelihood values when plot_type = "ll".
Significantly increased the test coverage and fixed minor bugs.
Changed color of time series plot from "lightgray" to "black" for better readability.
Added a title to the time series plot when calling plot.fHMM_model(plot_type = "ts"). Additionally, a time interval with arguments from and to can be selected to zoom into the data.

Added the following methods for an fHMM_model object: AIC(), BIC(), logLik(), nobs(), npar(), residuals().
The log-normal distribution can now be estimated by setting sdds = "lnorm" in the controls object.

Fixed bug in reorder_states() that did not order the fine-scale parameter sets when the coarse-scale order was changed.
Fixed bug in parameter_labels() that returned the wrong order of parameter labels.
Changed plot type of simulated data to lines.

In the vignette on controls, in the section about example specifications for controls, corrected sdds = "gamma(mu = -1|1)" to sdds = "gamma(mu = 0.5|2)" because mean of the Gamma distribution must be positive.
Added digits argument to print.fHMM_predict().
Fixed bug in reorder_states() that allowed for misspecification of state_order.
Added option to fit_model() to initialize at the estimates of another model (#73).