Random number generation for the truncated multivariate normal and Student t distribution. 
  Computes probabilities, quantiles and densities, 
  including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.

Simulation of random vectors from truncated multivariate normal 
  and t distributions based on the algorithms proposed by Yifang Li and Sujit K. Ghosh (2015) <doi:10.1080/15598608.2014.996690>.

Provides tools for performing Transition Network Analysis (TNA) to 
    study relational dynamics, including functions for building and plotting TNA 
    models, calculating centrality measures, and identifying dominant events and 
    patterns. TNA statistical techniques (e.g., bootstrapping and permutation 
    tests) ensure the reliability of observed insights and confirm that 
    identified dynamics are meaningful. See (Saqr et al., 2025) 
    <doi:10.1145/3706468.3706513> for more details on TNA.

Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.

Simplifies access to Tunisian government open data from <https://data.gov.tn/fr/>.
      Queries datasets by theme, author, or keywords, retrieves metadata, and gets structured results ready for analysis; all through the official 'CKAN' API.

Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.