Tools for importing, analyzing and visualizing ego-centered
    network data. Supports several data formats, including the export formats of
    'EgoNet', 'EgoWeb 2.0' and 'openeddi'. An interactive (shiny) app for the
    intuitive visualization of ego-centered networks is provided. Also included
    are procedures for creating and visualizing Clustered Graphs 
    (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).

Statistics and graphics for streamflow history,
    water quality trends, and the statistical modeling algorithm: Weighted
    Regressions on Time, Discharge, and Season (WRTDS).

Collection of functions to evaluate uncertainty of results from
    water quality analysis using the Weighted Regressions on Time Discharge and
    Season (WRTDS) method. This package is an add-on to the EGRET package that
    performs the WRTDS analysis. The WRTDS modeling
    method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>,
    and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The 
    paper describing the uncertainty and confidence interval calculations 
    is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.

Gene regulatory network constructed using combined score obtained from 
  individual network inference method. The combined score measures the significance 
  of edges in the ensemble network. Fisher's weighted method has been implemented to 
  combine the outcomes of different methods based on the probability values. 
  The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.

Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.