Computes a simple blinding index for randomized controlled trials introduced in the paper "A simple blinding index for randomized controlled trials" by Petroff, Bacak, Dagres, Dilk and Wachter, which has been submitted for publication.

Computes the sBIC for various singular model collections including:
    binomial mixtures, factor analysis models, Gaussian mixtures,
    latent forests, latent class analyses, and reduced rank regressions.

This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review).
   In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. 
   Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.

Implements sparse Bayesian learning method for QTL mapping and genome-wide association studies.

A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). 
  Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 'Bar-Hen et al.' (2020) <doi:10.48550/arXiv.1807.10138>.

Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) <doi:10.3150/14-BEJ645>.

An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.)