- Function
**baseline_model**:- processes the elements in the argument
*base_risk*for a fixed, random or predicted baseline model and passes the output to run_model or run_metareg to obtain the absolute risks for all interventions. - when a predicted baseline model is conducted, it returns a forest plot with the trial-specific and summary probability of an event for the selected reference intervention.

- processes the elements in the argument
- Function
**forestplot_metareg**:- upgraded plot that presents two forest plots side-by-side: (i) one on estimation and prediction from network meta-analysis and network meta-regression for a selected comparator intervention (allows comparison of these two analyses), and (ii) one on SUCRA values from both analyses. Both forest plots present results from network meta-regression for a selected value of the investigated covariate.

- Function
**league_table_absolute_user**:- (only for binary outcome) yields the same graph with
league_table_absolute, but the input is not
*rnmamod*object: the user defines the input and it includes the summary effect and corresponding (credible or confidence) interval for comparisons with a reference intervention. These results stem from a network meta-analysis conducted using another R-package or statistical software.

- (only for binary outcome) yields the same graph with
league_table_absolute, but the input is not
- Function
**robustness_index_user**:- calculates the robustness index for a sensitivity analysis performed
using the R-package
*netmeta*or*metafor*. The user defines the input and the function returns the robustness index. This function returns the same output with the**robustness_index**function.

- calculates the robustness index for a sensitivity analysis performed
using the R-package
- Function
**trans_quality**:- classifies a systematic review with multiple interventions as having low, unclear or high quality regarding the transitivity evaluation based on five quality criteria.

- Typos and links (for functions and packages) in the documentation are corrected.
- Function
**run_model**:- allows the user to define the reference intervention of the network
via the argument
*ref*; - (only for binary outcome) estimates the absolute risks for all
non-reference interventions using a selected baseline risk for the
reference intervention (argument
*base_risk*); - (only for binary outcome) estimates the relative risks and risk difference as functions of the estimated absolute risks.

- allows the user to define the reference intervention of the network
via the argument
- Function
**league_table_absolute**:- (only for binary outcome) it presents the absolute risks per 1000 participants in main diagonal, the odds ratio on the upper off-diagonals, and the risk difference per 1000 participants in the lower off-diagonals;
- allow the user to select the interventions to present via the
argument
*show*(ideal for very large networks that make the league table cluttered).

- Functions
**league_heatmap**and**league_heatmap_pred**:- allow the user to select the interventions to present via the
argument
*show*(ideal for very large networks that make the league table cluttered); - allow the user to illustrate the results of two outcomes for the same model (i.e. via run_model or run_metareg) or the results of two models on the same outcome (applicable for: (i) run_model versus run_metareg, and (ii) run_model versus run_series_meta).

- allow the user to select the interventions to present via the
argument
- Functions
**series_meta_plot**and**nodesplit_plot**:- present the extent of heterogeneity in the forest plot of tau using different colours for low, reasonable, fairly high, and fairly extreme tau (the classification has been suggested by Spiegelhalter et al., 2004; ISBN 0471499757).

- First CRAN submission.