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Lightweight and Feature Complete Unit Testing Framework
Provides a lightweight (zero-dependency) and easy to use unit testing framework. Main features: install tests with the package. Test results are treated as data that can be stored and manipulated. Test files are R scripts interspersed with test commands, that can be programmed over. Fully automated build-install-test sequence for packages. Skip tests when not run locally (e.g. on CRAN). Flexible and configurable output printing. Compare computed output with output stored with the package. Run tests in parallel. Extensible by other packages. Report side effects.
Approximate String Matching, Fuzzy Text Search, and String Distance Functions
Implements an approximate string matching version of R's native
'match' function. Also offers fuzzy text search based on various string
distance measures. Can calculate various string distances based on edits
(Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q-
gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An
implementation of soundex is provided as well. Distances can be computed between
character vectors while taking proper care of encoding or between integer
vectors representing generic sequences. This package is built for speed and
runs in parallel by using 'openMP'. An API for C or C++ is exposed as well.
Reference: MPJ van der Loo (2014)
Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.
Data Validation Infrastructure
Declare data validation rules and data quality indicators;
confront data with them and analyze or visualize the results.
The package supports rules that are per-field, in-record,
cross-record or cross-dataset. Rules can be automatically
analyzed for rule type and connectivity. See also Van der Loo
and De Jonge (2018)
Software Option Settings Manager for R
Provides option settings management that goes beyond R's default 'options' function. With this package, users can define their own option settings manager holding option names, default values and (if so desired) ranges or sets of allowed option values that will be automatically checked. Settings can then be retrieved, altered and reset to defaults with ease. For R programmers and package developers it offers cloning and merging functionality which allows for conveniently defining global and local options, possibly in a multilevel options hierarchy. See the package vignette for some examples concerning functions, S4 classes, and reference classes. There are convenience functions to reset par() and options() to their 'factory defaults'.
Manipulation of Linear Systems of (in)Equalities
Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities.
Parsing, Applying, and Manipulating Data Cleaning Rules
Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package.
Easy to use interfaces to a number of imputation methods that fit in the not-a-pipe operator of the 'magrittr' package.
Univariate Outlier Detection
Detect outliers in one-dimensional data.
Track Changes in Data
A framework that allows for easy logging of changes in data. Main features: start tracking changes by adding a single line of code to an existing script. Track changes in multiple datasets, using multiple loggers. Add custom-built loggers or use loggers offered by other packages.