Analyze Multinomial Processing Tree Models

Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.

Test Suite for 'Future API' Backends

Backends implementing the 'Future' API , as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API. The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines.

Configural Frequencies Analysis Using Log-Linear Modeling

Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.

Improved Allele-Specific Copy Number of SNP Microarrays for Downstream Segmentation

The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina.

Fast Implementation of the Diffusion Decision Model

Provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, ) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, ; Gondan, Blurton, & Kesselmeier, 2014, ; Blurton, Kesselmeier, & Gondan, 2017, ; Hartmann & Klauer, 2021, ) plus new approximations. All approximations are implemented purely in 'C++' providing faster speed than existing packages.

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.

Multivariate ARIMA and ARIMA-X Analysis

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

Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

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/>.