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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.
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'.
Univariate Outlier Detection
Detect outliers in one-dimensional data.
Deductive Correction, Deductive Imputation, and Deterministic Correction
A collection of methods for automated data cleaning where all actions are logged.
Locate Errors with Validation Rules
Errors in data can be located and removed using validation rules from package 'validate'.
Checking and Simplifying Validation Rule Sets
Rule sets with validation rules may contain redundancies or contradictions. Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with 'validate'.
Trends and Indices for Monitoring Data
The TRIM model is widely used for estimating growth and decline of animal populations based on (possibly sparsely available) count data. The current package is a reimplementation of the original TRIM software developed at Statistics Netherlands by Jeroen Pannekoek. See < https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d> for more information about TRIM.
Useful functions for visual word recognition research
Functions and data for use in visual word recognition research: Computation of neighbors (Hamming and Levenshtein distances), average distances to neighbors (e.g., OLD20), and Coltheart's N. Also includes the LD1NN algorithm to detect bias in the composition of a lexical decision task. Most of the functions support parallel execution. Supplies wordlists for several languages. Uses the string distance functions from the stringdist package by Mark van der Loo.
Extending 'dendrogram' Functionality in R
Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.