**Port of the ‘Scilab’ ‘n1qn1’ Module for Unconstrained BFGS Optimization** (**n1qn1**)

Provides ‘Scilab’ ‘n1qn1’, or Quasi-Newton BFGS ‘qn’ without constraints. This takes more memory than traditional L-BFGS. This routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the ‘Scilab’ optimization documentation located at <http://…/optimization_in_scilab.pdf>.

**Bayesian Calibrations of P-Values** (**pCalibrate**)

Implements transformations of P-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2017, On p-values and Bayes factors, Annual Review of Statistics and Its Application, 5, to appear). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test of overall significance in the linear model.

**TBF Methodology Extension for Multinomial Outcomes** (**TBFmultinomial**)

Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].

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