Implements HSROC (hierarchical summary receiver operating characteristic) model developed by Ma, Lian, Chu, Ibrahim, and Chen (2018) <doi:10.1093/biostatistics/kxx025>
  and hierarchical model developed by Lian, Hodges, and Chu (2019) <doi:10.1080/01621459.2018.1476239> for performing meta-analysis for 1-5 diagnostic tests to simultaneously 
    compare multiple tests within a missing data framework. This package evaluates the accuracy of 
    multiple diagnostic tests and also gives graphical representation of the results.

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.