**Add Formula Interfaces to Modelling Functions** (**formulize**)

Automatically generates wrappers for modelling functions that accept data as a data matrix X and a data vector y and produces a wrapper that allows users to specify input data with a formula and a data frame. In addition to generating formula interfaces, users may also generated wrapper S3 generics.

**Statistical Methods for Modeling Operational Risk** (**OpVaR**)

Functions for modeling operational (value-at-)risk. The implementation comprises functions for modeling loss frequencies and loss severities with plain, mixed (Frigessi et al. (2012) <doi:10.1023/A:1024072610684>) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (Ergashev et al. (2013) <doi:10.21314/JOP.2013.131>). In particular, the parametrization of tail distributions includes fitting of Tukey-type distributions (Kuo and Headrick (2014) <doi:10.1155/2014/645823>). Furthermore, the package contains the modeling of bivariate dependencies between loss severities and frequencies, Monte Carlo simulation for total loss estimation as well as a closed-form approximation based on Degen (2010) <doi:10.21314/JOP.2010.084> to determine the value-at-risk.

**Add Trendline of Basic Regression Models to Plot** (**basicTrendline**)

Add trendline of basic linear or nonlinear regression models and show equation to plot as simple as possible.

**Rapid Automatic Keyword Extraction (RAKE) Algorithm** (**rapidraker**)

A ‘Java’ implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.

**A High-Level R Interface for Neural Nets** (**kerasformula**)

Adds a high-level interface for ‘keras’ neural nets. kms() fits neural net and accepts R formulas to aid data munging and hyperparameter selection. kms() can optionally accept a compiled keras_sequential_model() from ‘keras’. kms() accepts a number of parameters (like loss and optimizer) and splits the data into sparse test and training matrices. kms() returns a single object with predictions, a confusion matrix, and function call details.

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