Statistical Inference for Online Learning and Stochastic Approximation via HiGrad (higrad)
Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm, a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD). In addition, this method attaches a confidence interval to assess the uncertainty of its predictions. See Su and Zhu (2018) <arXiv:1802.04876> for details.

Create Values for Human Consumption (humanize)
An almost direct port of the ‘python’ ‘humanize’ package <https://…/humanize>. This package contains utilities to convert values into human readable forms.

Zipf Extended Distributions (zipfextR)
Implementation of three extensions of the Zipf distribution: the Marshall-Olkin Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) <arXiv:1304.4540>, the Zipf-Poisson Extreme (Zipf-PE) and the Zipf-Poisson Stopped Sum (Zipf-PSS) distributions. In log-log scale, the two first extensions allow for top-concavity and top-convexity while the third one only allows for top-concavity. All the extensions maintain the linearity associated with the Zipf model in the tail.

Fast Imputations Using ‘Rcpp’ and ‘Armadillo’ (miceFast)
Fast imputations under the object-oriented programming paradigm. There was used quantitative models with a closed-form solution. Thus package is based on linear algebra operations. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used.

Classification Model Charts (Modelcharts)
Provides two important functions for producing Gain chart and Lift chart for any classification model.