Enables users to retrieve data, meta-data, and codebooks from
    <https://nettskjema.no/>. The data from the API is richer than from
    the online data portal. This package is not developed
    by the University of Oslo IT. Mowinckel (2021) <doi:10.5281/zenodo.4745481>.

Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon 'igraph'.

Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) <doi:10.2139/ssrn.4619531>. The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps.

Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.

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.

We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.