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Bayesian Networks & Path Analysis
This project aims to enable the method of Path Analysis to infer causalities
from data. For this we propose a hybrid approach, which uses Bayesian network
structure learning algorithms from data to create the input file for creation of a
PA model. The process is performed in a semi-automatic way by our intermediate
algorithm, allowing novice researchers to create and evaluate their own PA models
from a data set. The references used for this project are:
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.
Twitter Conversation Networks and Analysis
Collects tweets and metadata for threaded conversations and generates networks.
Network Strengths and Weaknesses Analysis
A set of functions for studying network robustness, resilience and vulnerability.
Environmental Phillips Curve Analysis with Multiple Instrumental Variables and Networks
Comprehensive toolkit for Environmental Phillips Curve analysis
featuring multidimensional instrumental variable creation, transfer entropy
causal discovery, network analysis, and state-of-the-art econometric methods.
Implements geographic, technological, migration, geopolitical, financial,
and natural risk instruments with robust diagnostics and visualization.
Provides 24 different instrumental variable approaches with empirical validation.
Methods based on Phillips (1958)
Classes for Relational Data
Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
Bayesian Meta-Analysis and Network Meta-Analysis
Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015)
Motif Analysis in Multi-Level Networks
Tools for motif analysis in multi-level networks. Multi-level networks combine multiple networks in one, e.g. social-ecological networks. Motifs are small configurations of nodes and edges (subgraphs) occurring in networks. 'motifr' can visualize multi-level networks, count multi-level network motifs and compare motif occurrences to baseline models. It also identifies contributions of existing or potential edges to motifs to find critical or missing edges. The package is in many parts an R wrapper for the excellent 'SESMotifAnalyser' 'Python' package written by Tim Seppelt.
Bayesian Package for Network Changepoint Analysis
Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. Version 1.1.0 includes high-performance C++ implementations via 'Rcpp'/'RcppArmadillo' for 5-15x faster MCMC sampling, along with modern 'ggplot2'-based visualizations with colorblind-friendly palettes.
Analysis of Diffusion and Contagion Processes on Networks
Empirical statistical analysis, visualization and simulation of
diffusion and contagion processes on networks. The package implements algorithms
for calculating network diffusion statistics such as transmission rate, hazard
rates, exposure models, network threshold levels, infectiousness (contagion),
and susceptibility. The package is inspired by work published in Valente,
et al., (2015)
Detecting Outliers in Network Meta-Analysis
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.