| +.lws_table | Combine 'lws_table' objects | 
| agk.test | Andreou, Ghysels, Kourtellos LM test | 
| almonp | Almon polynomial MIDAS weights specification | 
| almonp_gradient | Gradient function for Almon polynomial MIDAS weights | 
| amidas_table | Weight and lag selection table for aggregates based MIDAS regression model | 
| amweights | Weights for aggregates based MIDAS regressions | 
| average_forecast | Average forecasts of MIDAS models | 
| check_mixfreq | Check data for MIDAS regression | 
| coef.midas_nlpr | Extract coefficients of MIDAS regression | 
| coef.midas_r | Extract coefficients of MIDAS regression | 
| coef.midas_sp | Extract coefficients of MIDAS regression | 
| deriv_tests | Check whether non-linear least squares restricted MIDAS regression problem has converged | 
| deriv_tests.midas_r | Check whether non-linear least squares restricted MIDAS regression problem has converged | 
| deviance.midas_nlpr | Non-linear parametric MIDAS regression model deviance | 
| deviance.midas_r | MIDAS regression model deviance | 
| deviance.midas_sp | Semi-parametric MIDAS regression model deviance | 
| dmls | MIDAS lag structure for unit root processes | 
| expand_amidas | Create table of weights, lags and starting values for Ghysels weight schema | 
| expand_weights_lags | Create table of weights, lags and starting values | 
| extract.midas_r | Extract coefficients and GOF measures from MIDAS regression object | 
| fitted.midas_nlpr | Fitted values for non-linear parametric MIDAS regression model | 
| fitted.midas_sp | Fitted values for semi-parametric MIDAS regression model | 
| fmls | Full MIDAS lag structure | 
| forecast | Forecast MIDAS regression | 
| forecast.midas_r | Forecast MIDAS regression | 
| genexp | Generalized exponential MIDAS coefficients | 
| genexp_gradient | Gradient of generalized exponential MIDAS coefficient generating function | 
| get_estimation_sample | Get the data which was used to etimate MIDAS regression | 
| gompertzp | Normalized Gompertz probability density function MIDAS weights specification | 
| gompertzp_gradient | Gradient function for normalized Gompertz probability density function MIDAS weights specification | 
| hAhr_test | Test restrictions on coefficients of MIDAS regression using robust version of the test | 
| hAh_test | Test restrictions on coefficients of MIDAS regression | 
| harstep | HAR(3)-RV model MIDAS weights specification | 
| harstep_gradient | Gradient function for HAR(3)-RV model MIDAS weights specification | 
| hf_lags_table | Create a high frequency lag selection table for MIDAS regression model | 
| imidas_r | Restricted MIDAS regression with I(1) regressors | 
| lcauchyp | Normalized log-Cauchy probability density function MIDAS weights specification | 
| lcauchyp_gradient | Gradient function for normalized log-Cauchy probability density function MIDAS weights specification | 
| lf_lags_table | Create a low frequency lag selection table for MIDAS regression model | 
| lstr | Compute LSTR term for high frequency variable | 
| midas_auto_sim | Simulate simple autoregressive MIDAS model | 
| midas_lstr_plain | LSTR (Logistic Smooth TRansition) MIDAS regression | 
| midas_lstr_sim | Simulate LSTR MIDAS regression model | 
| midas_mmm_plain | MMM (Mean-Min-Max) MIDAS regression | 
| midas_mmm_sim | Simulate MMM MIDAS regression model | 
| midas_nlpr | Non-linear parametric MIDAS regression | 
| midas_nlpr.fit | Fit restricted MIDAS regression | 
| midas_pl_plain | MIDAS Partialy linear non-parametric regression | 
| midas_pl_sim | Simulate PL MIDAS regression model | 
| midas_qr | Restricted MIDAS quantile regression | 
| midas_r | Restricted MIDAS regression | 
| midas_r.fit | Fit restricted MIDAS regression | 
| midas_r_ic_table | Create a weight and lag selection table for MIDAS regression model | 
| midas_r_np | Estimate non-parametric MIDAS regression | 
| midas_r_plain | Restricted MIDAS regression | 
| midas_sim | Simulate simple MIDAS regression response variable | 
| midas_si_plain | MIDAS Single index regression | 
| midas_si_sim | Simulate SI MIDAS regression model | 
| midas_sp | Semi-parametric MIDAS regression | 
| midas_u | Estimate unrestricted MIDAS regression | 
| mls | MIDAS lag structure | 
| mlsd | MIDAS lag structure with dates | 
| mmm | Compute MMM term for high frequency variable | 
| modsel | Select the model based on given information criteria | 
| nakagamip | Normalized Nakagami probability density function MIDAS weights specification | 
| nakagamip_gradient | Gradient function for normalized Nakagami probability density function MIDAS weights specification | 
| nbeta | Normalized beta probability density function MIDAS weights specification | 
| nbetaMT | Normalized beta probability density function MIDAS weights specification (MATLAB toolbox compatible) | 
| nbetaMT_gradient | Gradient function for normalized beta probability density function MIDAS weights specification (MATLAB toolbox compatible) | 
| nbeta_gradient | Gradient function for normalized beta probability density function MIDAS weights specification | 
| nealmon | Normalized Exponential Almon lag MIDAS coefficients | 
| nealmon_gradient | Gradient function for normalized exponential Almon lag weights | 
| oos_prec | Out-of-sample prediction precision data on simulation example | 
| plot_lstr | Plot MIDAS coefficients | 
| plot_midas_coef | Plot MIDAS coefficients | 
| plot_midas_coef.midas_nlpr | Plot MIDAS coefficients | 
| plot_midas_coef.midas_r | Plot MIDAS coefficients | 
| plot_sp | Plot non-parametric part of the single index MIDAS regression | 
| polystep | Step function specification for MIDAS weights | 
| polystep_gradient | Gradient of step function specification for MIDAS weights | 
| predict.midas_nlpr | Predict method for non-linear parametric MIDAS regression fit | 
| predict.midas_r | Predict method for MIDAS regression fit | 
| predict.midas_sp | Predict method for semi-parametric MIDAS regression fit | 
| prep_hAh | Calculate data for hAh_test and hAhr_test | 
| rvsp500 | Realized volatility of S&P500 index | 
| select_and_forecast | Create table for different forecast horizons | 
| simulate | Simulate MIDAS regression response | 
| simulate.midas_r | Simulate MIDAS regression response | 
| split_data | Split mixed frequency data into in-sample and out-of-sample | 
| update_weights | Updates weights in MIDAS regression formula | 
| UScpiqs | US quartely seasonaly adjusted consumer price index | 
| USeffrw | US weekly effective federal funds rate. | 
| USpayems | United States total employment non-farms payroll, monthly, seasonally adjusted. | 
| USqgdp | United States gross domestic product, quarterly, seasonaly adjusted annual rate. | 
| USrealgdp | US annual gross domestic product in billions of chained 2005 dollars | 
| USunempr | US monthly unemployment rate | 
| weights_table | Create a weight function selection table for MIDAS regression model |