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.

Various Plotting Functions

Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.

Bridge Sampling for Marginal Likelihoods and Bayes Factors

Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, < http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>). Gronau, Singmann, & Wagenmakers (2020) .

Data Only: Algorithmic Complexity of Short Strings (Computed via Coding Theorem Method)

Data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on TMs with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states of the TMs.

The R to MOSEK Optimization Interface

This is a meta-package designed to support the installation of Rmosek (>= 6.0) and bring the optimization facilities of MOSEK (>= 6.0) to the R-language. The interface supports large-scale optimization of many kinds: Mixed-integer and continuous linear, second-order cone, exponential cone and power cone optimization, as well as continuous semidefinite optimization. Rmosek and the R-language are open-source projects. MOSEK is a proprietary product, but unrestricted trial and academic licenses are available.

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.

Generalized Linear Models with Clustering

Binomial and Poisson regression for clustered data, fixed and random effects with bootstrapping.

Fast Pseudo Random Number Generators

Several fast random number generators are provided as C++ header only libraries: The PCG family by O'Neill (2014 < https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>) as well as the Xoroshiro / Xoshiro family by Blackman and Vigna (2021 ). In addition fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang (2000, ). The fast sampling methods support unweighted sampling both with and without replacement. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+/++/** and Xoshiro256+/++/** as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011, ) as provided by the package 'sitmo'.

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). 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() who performs the multiverse analysis in one call.

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.