Readable String Abbreviation

Strings are abbreviated to at least "minlength" characters, such that they remain unique (if they were). The abbreviations should be recognisable.

Encrypts and Decrypts Strings

Encrypts and decrypts strings using either the Caesar cipher or a pseudorandom number generation (using set.seed()) method.

Interpreted String Literals

An implementation of interpreted string literals. Based on the 'glue' package by Hester & Bryan (2024) but with a focus on efficiency and simplicity at a cost of flexibility.

Normalization of 'JSON' Strings

Provides a function allowing to normalize a 'JSON' string, for example by adding double quotes around the keys when they are missing. Also provides 'RStudio' addins for the same purpose.

Fuzzy String Matching

Fuzzy string matching implementation of the 'fuzzywuzzy' < https://github.com/seatgeek/fuzzywuzzy> 'python' package. It uses the Levenshtein Distance < https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.

String Patterns and Statistical Differences Between Two Groups of Strings

Methods include converting series of event names to strings, finding common patterns in a group of strings, discovering "unique" patterns when comparing two groups of strings as well as the number and starting position of each pattern in each string, obtaining transition matrix, computing transition entropy, statistically comparing the difference between two groups of strings, and clustering string groups. Event names can be any action names or labels such as events in log files or areas of interest (AOIs) in eye tracking research. An R Shiny application is available on GitHub.

Handle Strings as Vectors of Characters

Creates a new chars class which looks like a string but is actually a vector of individual characters, making 'strings' iterable. This class enables vector operations on 'strings' such as reverse, sort, head, and set operations.

'Python' Style String Functions

A comprehensive set of string manipulation functions based on those found in 'Python' without relying on 'reticulate'. It provides functions that intend to (1) make it easier for users familiar with 'Python' to work with strings, (2) reduce the complexity often associated with string operations, (3) and enable users to write more readable and maintainable code that manipulates strings.

Algorithmic Complexity for Short Strings

Main functionality is to provide the algorithmic complexity for short strings, an approximation of the Kolmogorov Complexity of a short string using the coding theorem method (see ?acss). The database containing the complexity is provided in the data only package acss.data, this package provides functions accessing the data such as prob_random returning the posterior probability that a given string was produced by a random process. In addition, two traditional (but problematic) measures of complexity are also provided: entropy and change complexity.

Adaptive Builder for Formatted Strings

Provides a set of functions to facilitate building formatted strings under various replacement rules: C-style formatting, variable-based formatting, and number-based formatting. C-style formatting is basically identical to built-in function 'sprintf'. Variable-based formatting allows users to put variable names in a formatted string which will be replaced by variable values. Number-based formatting allows users to use index numbers to represent the corresponding argument value to appear in the string.