| block_cv | Use Block Cross-Validation to Evaluate Models |
| coef.quadVAR | Estimate lag-1 quadratic vector autoregression models |
| coef.true_model_4_emo | True model for 4-emotion model |
| compare_4_emo | Compare estimated model with true model for 4-emotion model |
| find_index | Find index of data that satisfies certain conditions |
| get_adj_mat | Extract the adjacency matrix from a quadVAR object. |
| linear_quadVAR_network | Linearize a quadVAR object to produce a network. |
| partial_plot | Make a partial plot of a variable in a model This function takes a quadVAR model as input, and returns a plot of the partial effect of a variable on the dependent variable (controlling all other variables and the intercept), for higher and lower levels of the moderator variable split by the median. |
| plot.linear_quadVAR_network | Linearize a quadVAR object to produce a network. |
| plot.quadVAR | Estimate lag-1 quadratic vector autoregression models |
| predict.quadVAR | Predict the values of the dependent variables using the quadVAR model |
| print.coef_quadVAR | Estimate lag-1 quadratic vector autoregression models |
| print.quadVAR | Estimate lag-1 quadratic vector autoregression models |
| print.true_model_4_emo | True model for 4-emotion model |
| quadVAR | Estimate lag-1 quadratic vector autoregression models |
| quadVAR_to_dyn_eqns | Transform a quadVAR object to a list of dynamic equations. |
| sim_4_emo | Simulate a 4-emotion model |
| summary.quadVAR | Estimate lag-1 quadratic vector autoregression models |
| true_model_4_emo | True model for 4-emotion model |
| tune.fit | Using the *glmnet* and *ncvreg* packages, fits a Generalized Linear Model or Cox Proportional Hazards Model using various methods for choosing the regularization parameter lambda |