GAfit                   Genetic algorithm for preliminary estimation of
                        GMVAR model
GMVAR                   Create object of class 'gmvar' defining a GMVAR
                        model
aa_gmvarkit             gmvarkit: Estimate Gaussian Mixture Vector
                        Autoregressive (GMVAR) model
add_data                Add data to object of class 'gmvar' defining a
                        GMVAR model
all_pos_ints            Check whether all arguments are positive scalar
                        whole numbers
calc_gradient           Calculate gradient or Hessian matrix
change_parametrization
                        Change parametrization of the parameter vector
change_regime           Change regime parameters *upsilon_{m}* =
                        (phi_{m,0},*phi_{m}*,sigma_{m}) of the given
                        parameter vector.
check_constraints       Check the constraint matrix has the correct
                        form
check_data              Check the data is in the correct form
check_gmvar             Checks whether the given object has class
                        attribute "gmvar"
check_null_data         Checks whether the given object contains data
                        or not
check_pMd               Check that p, M and d are correctly set
check_parameters        Check that the given parameter vector satisfies
                        model assumptions
diagnostic_plot         Quantile residual diagnostic plot for GMVAR
                        model
dlogmultinorm           Calculate logarithms of multiple multivariate
                        normal densities with varying mean and constant
                        covariance matrix
eurusd                  Euro area and U.S. long-term government bond
                        yields and Euro-U.S. dollar exchange rate.
fitGMVAR                Two-phase maximum likelihood estimation of
                        GMVAR model
form_boldA              Form the ((dp)x(dp)) "bold A" matrices related
                        to the VAR processes
format_valuef           Function factory for value formatting
get_IC                  Calculate AIC, HQIC and BIC
get_boldA_eigens        Calculate absolute values of the eigenvalues of
                        the "bold A" matrices containing the AR
                        coefficients
get_regime_means        Calculate and return regime means mu_{m}
get_regime_means_int    Calculate and return regime means mu_{m}
get_test_Omega          Compute covariance matrix Omega used in
                        quantile residual tests
in_paramspace           Determine whether the parameter vector lies in
                        the parameter space or not
in_paramspace_int       Determine whether the parameter vector lies in
                        the parameter space or not
is_stationary           Check the stationary condition of given GMVAR
                        model
iterate_more            Maximum likelihood estimation of GMVAR model
                        with preliminary estimates
loglikelihood           Compute log-likelihood of GMVAR model using
                        parameter vector
loglikelihood_int       Compute the log-likelihood of Gaussian Mixture
                        Vector Autoregressive model
n_params                Calculate the number of parameters in GMVAR
                        model parameter vector
pick_Am                 Pick coefficient matrices
pick_Ami                Pick coefficient matrix
pick_Omegas             Pick covariance matrices
pick_allA               Pick coefficient all matrices
pick_all_phi0_A         Pick all phi_{m,0} or mu_{m} and A_{m}
                        parameter values from the given parameter
                        vector.
pick_alphas             Pick mixing weight parameters alpha_{m},
                        m=1,...,M from the given parameter vector.
pick_phi0               Pick phi_{m,0} or mu_{m}, m=1,..,M vectors from
                        the given parameter vector
pick_regime             Pick regime parameters *upsilon_{m}* =
                        (phi_{m,0},*phi_{m}*,sigma_{m}) from the given
                        parameter vector.
plot.gmvarpred          plot method for class 'gmvarpred' objects
plot.qrtest             Quantile residual tests
predict.gmvar           Predict method for class 'gmvar' objects
print.gmvarpred         print method for class 'gmvarpred' objects
print.gmvarsum          Summary print method from objects of class
                        'gmvarsum'
print_std_errors        Print standard errors of GMVAR model in the
                        same form as the model estimates are printed
quantile_residuals      Calculate multivariate quantile residuals of
                        GMVAR model
quantile_residuals_int
                        Calculate multivariate quantile residuals of
                        GMVAR model
random_coefmats         Create random VAR-model (dxd) coefficient
                        matrices A.
random_coefmats2        Create somewhat random stationary VAR model
                        (dxd) coefficient matrices A.
random_covmat           Create somewhat random VAR model error term
                        covariance matrix
random_ind              Create somewhat random mean-parametrized
                        parameter vector of GMVAR model, that may not
                        be stationary!
random_ind2             Create somewhat random parameter vector of
                        GMVAR model that is always stationary
reform_constrained_pars
                        Reform constrained parameter vector into the
                        "standard" form
reform_data             Reform data
regime_distance         Calculate "distance" between two (scaled)
                        regimes *upsilon_{m}* =
                        (phi_{m,0},*phi_{m}*,sigma_{m})
simulateGMVAR           Simulate from GMVAR process
smart_covmat            Create somewhat random VAR-model (dxd) error
                        term covariance matrix Omega fairly close to a
                        given *positive definite* covariance matrix
                        using (scaled) Wishart distribution.
smart_ind               Create somewhat random parameter vector of
                        GMVAR model fairly close to a given parameter
                        vector
sort_components         Sort components in parameter vector by mixing
                        weights into a decreasing order
standard_errors         Calculate standard errors for estimates of
                        GMVAR model
swap_parametrization    Swap the parametrization of object of class
                        'gmvar' defining a GMVAR model
unvec                   Reverse vectorization operator.
unvech                  Reverse operator of the parsimonious
                        vectorization operator 'vech()'.
vec                     Vectorization operator.
vech                    Parsimonious vectorization operator for
                        symmetric matrices.
