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.

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.

Fits a Fay Herriot Model

Inference techniques for Fay Herriot Model.

Interface to 'typeform' Results

An R interface to the 'typeform' < https://typeform.com> application program interface. Also provides functions for downloading your results.

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.

Data Set for the 'benchmarkme' Package

Crowd sourced benchmarks from running the 'benchmarkme' package.

Selection Threshold Optimized Empirically via Splitting

Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).

Datasets from the Datasaurus Dozen

The Datasaurus Dozen is a set of datasets with the same summary statistics. They retain the same summary statistics despite having radically different distributions. The datasets represent a larger and quirkier object lesson that is typically taught via Anscombe's Quartet (available in the 'datasets' package). Anscombe's Quartet contains four very different distributions with the same summary statistics and as such highlights the value of visualisation in understanding data, over and above summary statistics. As well as being an engaging variant on the Quartet, the data is generated in a novel way. The simulated annealing process used to derive datasets from the original Datasaurus is detailed in "Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing" .

Reliably Return the Source and Call Location of a Command

Robust and reliable functions to return informative outputs to console with the run or source location of a command. This can be from the 'RScript'/R terminal commands or 'RStudio' console, source editor, 'Rmarkdown' document and a Shiny application.