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Triadic Analysis of Affiliation Networks
Two principal tools are provided for the triadic analysis of
affiliation networks: triad census and triadic closure.
These include several variations on both classical tools
tailored to affiliation network structure; see
Opsahl (2013)
Analysis of Queueing Networks and Models
It provides versatile tools for analysis of birth and death based Markovian Queueing Models and Single and Multiclass Product-Form Queueing Networks. It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K, Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks, Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks and Single Channel Multiple Class Mixed Networks. Also it provides a B-Erlang, C-Erlang and Engset calculators. This work is dedicated to the memory of D. Sixto Rios Insua.
Sensitivity Analysis of Neural Networks
Analysis functions to quantify inputs importance in neural network models.
Functions are available for calculating and plotting the inputs importance and obtaining
the activation function of each neuron layer and its derivatives. The importance of a given
input is defined as the distribution of the derivatives of the output with respect to that
input in each training data point
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.