The network autocorrelation model (NAM) can be used for studying the degree of social influence 
    regarding an outcome variable based on one or more known networks. 
    The degree of social influence is quantified via the network autocorrelation parameters. In case of a single
    network, the Bayesian methods of Dittrich, Leenders, and Mulder
    (2017) <DOI:10.1016/j.socnet.2016.09.002> and Dittrich, Leenders, and Mulder (2019)
    <DOI:10.1177/0049124117729712> are implemented using a normal, flat, or independence  
    Jeffreys prior for the network autocorrelation. In the case of multiple 
    networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) 
    <DOI:10.1177/0081175020913899> are implemented using a multivariate normal prior for 
    the network autocorrelation parameters. Flat priors are implemented 
    for estimating the coefficients. For Bayesian testing of equality and order-constrained 
    hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) 
    <DOI:10.1111/bmsp.12110> is used with the posterior mean and posterior covariance 
    matrix of the NAM parameters based on flat priors as input.

A light-weight object-oriented system with 'python'-like syntax which supports multiple inheritances and incorporates a 'python'-like method resolution order.

A set of functions for doing analysis of A/B split test data and web metrics in general.

Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" <https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.

Implements functions that calculate upper prediction 
  bounds on the false discovery proportion (FDP) in the list of discoveries 
  returned by competition-based setups, implementing Ebadi et al. (2022)
  <arXiv:2302.11837>. Such setups include target-decoy competition (TDC) 
  in computational mass spectrometry and the knockoff construction in linear 
  regression (note this package typically uses the terminology of TDC). Included 
  is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the 
  simultaneous standardized and uniform bands. Requires 
  pre-computed Monte Carlo statistics available at 
  <https://github.com/uni-Arya/fdpbandsdata>. This data can be downloaded by
  running the command 'devtools::install_github("uni-Arya/fdpbandsdata")' in R
  and restarting R after installation. The size of this data is roughly 81Mb.