It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode  and valued one-mode and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) <doi:10.1111/bmsp.12192>).

Simplifies and automates the process of exploring and merging 
    data from relational databases. This package allows users to discover 
    table relationships, create a map of all possible joins, and generate 
    executable plans to merge data based on a structured metadata framework.

Implements the de-biased estimator for low-rank matrix completion and provides confidence intervals for entries of interest. See: by Chen et al. (2019) <doi:10.1073/pnas.1910053116>, Mai (2021) <arXiv:2103.11749>.

Perform model selection using distribution and probability-based methods,
	including standardized AIC, BIC, and AICc. These standardized information criteria
	allow one to perform model selection in a way similar to the prevalent "Rule of 2"
	method, but formalize the method to rely on probability theory. A novel goodness-of-fit
	procedure for assessing linear regression models is also available. This test relies on
	theoretical properties of the estimated error variance for a normal linear regression
	model, and employs a bootstrap procedure to assess the null hypothesis that the fitted
	model shows no lack of fit. For more information, see Koeneman and Cavanaugh (2023)
	<arXiv:2309.10614>. Functionality to perform all subsets linear or generalized linear
	regression is also available.

Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) <doi:10.18637/jss.v067.c03>.
  Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). 
  Relies on 'spatstat' for some core calculation.

It allows to learn the structure of univariate time series, learning parameters and forecasting. 
             Implements a model of Dynamic Bayesian Networks with temporal windows, 
             with collections of linear regressors for Gaussian nodes, 
             based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and 
             Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.