Provides customized forest plots for network meta-analysis incorporating direct, indirect, and NMA effects. Includes visualizations of evidence contributions through 
    proportion bars based on the hat matrix and evidence flow decomposition.

Performs network meta-analysis using integrated nested Laplace approximations ('INLA') 
             which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. 
             Includes methods to assess the heterogeneity and inconsistency in the network. 
             Contains more than ten different network meta-analysis dataset. 
             'INLA' package can be obtained from <https://www.r-inla.org>.

A set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis :
               - simple outlier and influential detection measures
               - outlier and influential detection measures by considering study deletion (shift the mean)
               - plots for outlier and influential detection measures
	       - Q-Q plot for network meta-analysis
               - Forward Search algorithm in network meta-analysis. 
               - forward plots to monitor statistics in each step of the forward search algorithm
               - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.

A graphical display of results from network meta-analysis (NMA). 
  It is suitable for outcomes like odds ratio (OR), risk ratio (RR), 
  risk difference (RD) and standardized mean difference (SMD). 
  It also has an option to visually display and compare 
  the surface under the cumulative ranking (SUCRA) of different treatments.

Methods to estimate finite-population parameters under nonresponse that is not missing at
    random (NMAR, nonignorable). Incorporates auxiliary information and user-specified response models,
    and supports independent samples and complex survey designs via objects from the 'survey' package.
    Provides diagnostics and optional variance estimates. For methodological background see
    Qin, Leung and Shao (2002) <doi:10.1198/016214502753479338> and Riddles, Kim and Im (2016) <doi:10.1093/jssam/smv047>.