Contextual Bayesian Anomaly Detection in R (cbar)
Detect contextual anomalies in time-series data with Bayesian data analysis. It focuses on determining a normal range of target value, and provides simple-to-use functions to abstract the outcome.

Make Fake Data (charlatan)
Make fake data, supporting addresses, person names, dates, times, colors, coordinates, currencies, digital object identifiers (‘DOIs’), jobs, phone numbers, ‘DNA’ sequences, doubles and integers from distributions and within a range.

Non-Standard ROC Curve Analysis (nsROC)
Tools for estimating Receiver Operating Characteristic (ROC) curves, building confidence bands, comparing several curves both for dependent and independent data, estimating the cumulative-dynamic ROC curve in presence of censored data, and performing meta-analysis studies, among others.

Tools for Tall Distributed Matrices (kazaam)
Many data science problems reduce to operations on very tall, skinny matrices. However, sometimes these matrices can be so tall that they are difficult to work with, or do not even fit into main memory. One strategy to deal with such objects is to distribute their rows across several processors. To this end, we offer an ‘S4’ class for tall, skinny, distributed matrices, called the ‘shaq’. We also provide many useful numerical methods and statistics operations for operating on these distributed objects. The naming is a bit ‘tongue-in-cheek’, with the class a play on the fact that ‘Shaquille’ ‘ONeal’ (‘Shaq’) is very tall, and he starred in the film ‘Kazaam’.

Survival Analysis in Health Economic Evaluation (survHE)
Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners).

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