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>).

Trains logistic regression model by discretizing continuous variables via gradient boosting approach. The proposed method tries to achieve a tradeoff between interpretation and prediction accuracy for logistic regression by discretizing the continuous variables. The variable binning is accomplished in a supervised fashion. The model trained by this package is still a single 
  logistic regression model, but not a sequence of logistic regression models. The fitted model
  object returned from the model training consists of two tables. One table is used to give the
  boundaries of bins for each continuous variable as well as the corresponding coefficients,
  and the other one is used for discrete variables. This package can also be used for binning
  continuous variables for other statistical analysis.

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.