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>.

Given the non-negative data and its distribution, the package estimates the rank parameter for Non-negative Matrix Factorization. The method is based on hypothesis testing, using a deconvolved bootstrap distribution to assess the significance level accurately despite the large amount of optimization error. The distribution of the non-negative data can be either Normal distributed or Poisson distributed.

Learning and inference over dynamic Bayesian networks of arbitrary 
    Markovian order. Extends some of the functionality offered by the 'bnlearn' 
    package to learn the networks from data and perform exact inference. 
    It offers three structure learning algorithms for dynamic Bayesian networks:
    Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014)
    <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021)
    <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform 
    forecasts of arbitrary length. A tool for visualizing the structure of the 
    net is also provided via the 'visNetwork' package.

This tool is for parsing public drug databases such as 'DrugBank' XML database <https://go.drugbank.com/>.
    The parsed data are then returned in a proper 'R' object called 'dvobject'.