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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