Statistics for Holland's Theory of Vocational Choice

Offers a convenient way to compute parameters in the framework of the theory of vocational choice introduced by J.L. Holland, (1997). A comprehensive summary to this theory of vocational choice is given in Holland, J.L. (1997). Making vocational choices. A theory of vocational personalities and work environments. Lutz, FL: Psychological Assessment.

Analysis of Large Affymetrix Microarray Data Sets

A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006.

A 'Future' API for Parallel Processing using 'mirai'

Implementation of the 'Future' API on top of the 'mirai' package . By using this package, you get to take advantage of the benefits of 'mirai' plus everything else that 'future' and the 'Futureverse' adds on top of it. It allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local machine or across remote machines. Contrary to back-ends relying on the 'parallel' package (e.g. 'multisession') and socket connections, 'mirai_cluster' and 'mirai_multisession', provided here, can run more than 125 parallel R processes. As a reminder, regardless which future backend is used by the user, the code does not have to change, it gives identical results, and behaves exactly the same.

An Efficient and Deterministic Method for Identifying Topological Domains in Genomes

The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 ). The authors published an implementation of their method as an R script (two different versions; also available in this package). This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation. It also provides a small number of bug fixes to the original code.

Multivariate ARIMA and ARIMA-X Analysis

Multivariate ARIMA and ARIMA-X estimation using Spliid's algorithm (marima()) and simulation (marima.sim()).

Response Time Distributions

Provides response time distributions (density/PDF, distribution function/CDF, quantile function, and random generation): (a) Ratcliff diffusion model (Ratcliff & McKoon, 2008, ) based on C code by Andreas and Jochen Voss and (b) linear ballistic accumulator (LBA; Brown & Heathcote, 2008, ) with different distributions underlying the drift rate.

Track Estimation using YAPS (Yet Another Positioning Solver)

Estimate tracks of animals tagged with acoustic transmitters. 'yaps' was introduced in 2017 as a transparent open-source tool to estimate positions of fish (and other aquatic animals) tagged with acoustic transmitters. Based on registrations of acoustic transmitters on hydrophones positioned in a fixed array, 'yaps' enables users to synchronize the collected data (i.e. correcting for drift in the internal clocks of the hydrophones/receivers) and subsequently to estimate tracks of the tagged animals. The paper introducing 'yaps' is available in open access at Baktoft, Gjelland, Økland & Thygesen (2017) . Also check out our cookbook with a completely worked through example at Baktoft, Gjelland, Økland, Rehage, Rodemann, Corujo, Viadero & Thygesen (2019) . Additional tutorials will eventually make their way onto the project website at < https://baktoft.github.io/yaps/>.

Multiverse Analysis of Multinomial Processing Tree Models

Statistical or cognitive modeling usually requires a number of more or less arbitrary choices creating one specific path through a 'garden of forking paths'. The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, ) offers a principled alternative in which results for all possible combinations of reasonable modeling choices are reported. MPTmultiverse performs a multiverse analysis for multinomial processing tree (MPT, Riefer & Batchelder, 1988, ) models combining maximum-likelihood/frequentist and Bayesian estimation approaches with different levels of pooling (i.e., data aggregation) as described in Singmann et al. (2024, ). For the frequentist approaches, no pooling (with and without parametric or nonparametric bootstrap) and complete pooling are implemented using MPTinR < https://cran.r-project.org/package=MPTinR>. For the Bayesian approaches, no pooling, complete pooling, and three different variants of partial pooling are implemented using TreeBUGS < https://cran.r-project.org/package=TreeBUGS>. The main function is fit_mpt() which performs the multiverse analysis in one call.

Convert Gene IDs Between Each Other and Fetch Annotations from Biomart

Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in 'AnnotationDbi' in convertId2() but that function is only provided as fallback mechanism for the most common use cases in data analysis. The main function in the package is convert.bm() which queries BioMart using the full capacity of the API provided through the 'biomaRt' package. Presets and defaults are provided for convenience but all "marts", "filters" and "attributes" can be set by the user. Function convert.alias() converts Gene Symbols to Aliases and vice versa and function likely_symbol() attempts to determine the most likely current Gene Symbol.

Automated Transcriptome Classifier Pipeline: Comprehensive Transcriptome Analysis

An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. We rely on the 'pamr' < http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.