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.

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.

Various Plotting Functions

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

Generalized Linear Models with Clustering

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

Sequence Globally Unique Identifier (SEGUID) Checksums

Implementation of the original Sequence Globally Unique Identifier (SEGUID) algorithm [Babnigg and Giometti (2006) ] and SEGUID v2 (< https://www.seguid.org>), which extends SEGUID v1 with support for linear, circular, single- and double-stranded biological sequences, e.g. DNA, RNA, and proteins.

A Probe-Level Data File Format Used by 'aroma.affymetrix' [deprecated]

DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities. Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets). In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit. This speeds up the read substantially. This package is supporting the Aroma framework and should not be used elsewhere.)

Easily Install and Load the 'Futureverse'

The 'Futureverse' is a set of packages for parallel and distributed process with the 'future' package at its core, cf. Bengtsson (2021) . This package is designed to make it easy to install and load multiple 'Futureverse' packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each 'Futureverse' package as a dependency as needed.

Friendly R Startup Configuration

Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files. This makes it possible to keep private / secret environment variables separate from other environment variables. It also makes it easier to share specific startup settings by simply copying a file to a directory.

Methods for Accessing Huge Amounts of Data [deprecated]

DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor). The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system. This way the size limit of the data is set by the file system and not the memory.)

Copy-Number Analysis of Large Microarray Data Sets

Methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.