Formal Parser and Related Tools for R Markdown Documents

An implementation of a formal grammar and parser for R Markdown documents using the Boost Spirit X3 library. It also includes a collection of high level functions for working with the resulting abstract syntax tree.

Scans R Projects for Vulnerable Third Party Dependencies

Collects a list of your third party R packages, and scans them with the 'OSS' Index provided by 'Sonatype', reporting back on any vulnerabilities that are found in the third party packages you use.

Markdown Parser Implemented using the 'MD4C' Library

Provides an R wrapper for the 'MD4C' (Markdown for 'C') library. Functions exist for parsing markdown ('CommonMark' compliant) along with support for other common markdown extensions (e.g. GitHub flavored markdown, 'LaTeX' equation support, etc.). The package also provides a number of higher level functions for exploring and manipulating markdown abstract syntax trees as well as translating and displaying the documents.

Two-Sample Test of many Functional Means using the Energy Method

Given two samples of size n_1 and n_2 from a data set where each sample consists of K functional observations (channels), each recorded on T grid points, the function energy method implements a hypothesis test of equality of channel-wise mean at each channel using the bootstrapped distribution of maximum energy to control family wise error. The function energy_method_complex accomodates complex valued functional observations.

Optimization via Subsampling (OPTS)

Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.

Statistical Inference on Lineup Fairness

Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? A lineup is supposed to consist of a suspect and a number of additional members, or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup measures are descriptive, in the first instance, but since the earliest articles in the literature researchers have recognised the importance of reasoning inferentially about them. This package contains functions to compute various properties of laboratory or police lineups, and is intended for use by researchers in forensic psychology and/or eyewitness testimony research. Among others, the r4lineups package includes functions for calculating lineup proportion, functional size, various estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity ratio, ROC curves for confidence x accuracy data and the degree of similarity of faces in a lineup.

Easy Dockerfile Creation from R

Build a Dockerfile straight from your R session. 'dockerfiler' allows you to create step by step a Dockerfile, and provide convenient tools to wrap R code inside this Dockerfile.

String Diff, Match, and Patch Utilities

A wrapper for Google's 'diff-match-patch' library. It provides basic tools for computing diffs, finding fuzzy matches, and constructing / applying patches to strings.

R Interface to the Pushbullet Messaging Service

An R interface to the Pushbullet messaging service which provides fast and efficient notifications (and file transfer) between computers, phones and tablets. An account has to be registered at the site < https://www.pushbullet.com> site to obtain a (free) API key.

Data Set for the 'benchmarkme' Package

Crowd sourced benchmarks from running the 'benchmarkme' package.