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cff-version: 1.2.0
message: 'To cite package "MR.RGM" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'MR.RGM: Multivariate Bidirectional Mendelian Randomization Networks'
version: 0.0.3
doi: 10.32614/CRAN.package.MR.RGM
abstract: Addressing a central challenge encountered in Mendelian randomization (MR)
  studies, where MR primarily focuses on discerning the effects of individual exposures
  on specific outcomes and establishes causal links between them. Using a network-based
  methodology, the intricacy involving interdependent outcomes due to numerous factors
  has been tackled through this routine. Based on Ni et al. (2018) <https://doi.org/10.1214/17-BA1087>,
  'MR.RGM' extends to a broader exploration of the causal landscape by leveraging
  on network structures and involves the construction of causal graphs that capture
  interactions between response variables and consequently between responses and instrument
  variables. 'MR.RGM' facilitates the navigation of various data availability scenarios
  effectively by accommodating three input formats, i.e., individual-level data and
  two types of summary-level data. In the process, causal effects, adjacency matrices,
  and other essential parameters of the complex biological networks, are estimated.
  Besides, 'MR.RGM' provides uncertainty quantification for specific network structures
  among response variables.
authors:
- family-names: Sarkar
  given-names: Bitan
  email: bitan@tamu.edu
- family-names: Ni
  given-names: Yang
  email: yni@stat.tamu.edu
repository: https://mrcieu.r-universe.dev
repository-code: https://github.com/bitansa/MR.RGM
commit: bf0168ac6fb58dc6870b635b8a682251093d1e23
url: https://github.com/bitansa/MR.RGM
contact:
- family-names: Sarkar
  given-names: Bitan
  email: bitan@tamu.edu