Trajectory Miner: a Sequence Analysis Toolkit

Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.

Query 'SWI'-'Prolog' from R

This R package connects to SWI-Prolog, < https://www.swi-prolog.org/>, so that R can send deterministic and non-deterministic queries to prolog (consult, query/submit, once, findall).

Translate R Expressions to 'MathML' and 'LaTeX'/'MathJax'

Translate R expressions to 'MathML' or 'MathJax'/'LaTeX' so that they can be rendered in R markdown documents and shiny apps. This package depends on R package 'rolog', which requires an installation of the 'SWI'-'Prolog' runtime either from 'swi-prolog.org' or from R package 'rswipl'.

Accessing 'SimFin' Data

Through simfinapi, you can intuitively access the 'SimFin' Web-API (< https://www.simfin.com/>) to make 'SimFin' data easily available in R. To obtain an 'SimFin' API key (and thus to use this package), you need to register at < https://app.simfin.com/login>.

Embed 'SWI'-'Prolog'

Interface to 'SWI'-'Prolog', < https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.

Moderation Analysis for Two-Instance Repeated Measures Designs

Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" .

Greedy Set Cover

A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.

Explicitly Qualifying Namespaces by Automatically Adding 'pkg::' to Functions

Automatically adding 'pkg::' to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the 'RStudio' Markers pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used for checking which packages are actually used in a project.

Infinitesimally Robust Estimators for Preprocessing -Omics Data

Functions for the determination of optimally robust influence curves and estimators for preprocessing omics data, in particular gene expression data (Kohl and Deigner (2010), ).

Optimally Robust Influence Curves for Regression and Scale

Functions for the determination of optimally robust influence curves in case of linear regression with unknown scale and standard normal distributed errors where the regressor is random.