**Create n-Dimensional, Quasi-Proportional Venn Diagrams** (**nVennR**)

Provides an interface for the nVenn algorithm (Perez-Silva et al. 2018) <DOI:10.1093/bioinformatics/bty109>. This algorithm works for any number of sets, and usually yields pleasing and informative Venn diagrams with proportionality information. However, representing more than six sets takes a long time and is hard to interpret, unless many of the regions are empty. If you cannot make sense of the result, you may want to consider ‘UpSetR’ <https://…/README.html>.

**Estimation of the log Likelihood of the Saturated Model** (**lsm**)

When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.

**Multivariate Bayesian Model with Shrinkage Priors** (**MBSP**)

Implements a sparse Bayesian multivariate linear regression model using shrinkage priors from the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <arXiv:1711.07635>.

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