Pdist computes the euclidean distance between rows of a matrix X and rows of another matrix Y. Previously, this could be done by binding the two matrices together and calling dist, but this creates unnecessary computation by computing the distances between a row of X and another row of X, and likewise for Y. Pdist strictly computes distances across the two matrices, not within the same matrix, making computations significantly faster for certain use cases.

This package provides tools to compute and represent gene set enrichment or depletion from your data based on pre-saved maps from the Atlas of Cancer Signalling Networks (ACSN) or user imported maps. The gene set enrichment can be run with hypergeometric test or Fisher exact test, and can use multiple corrections. Visualization of data can be done either by barplots or heatmaps.

This package provides functions for assessing the replication/preservation of a network module's topology across datasets through permutation testing.

With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines CppAD (C++ automatic differentiation), Eigen (templated matrix-vector library) and CHOLMOD (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through BLAS and parallel user templates.

This package implements a successive halving and hyperband optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk.

This package contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. The algorithms are based of those of Walter Zucchini.

This package is a port of the gWidgets2 API for the tcltk package.

Testing and documenting code that communicates with remote servers can be painful. Dealing with authentication, server state, and other complications can make testing seem too costly to bother with. But it doesn't need to be that hard. This package enables one to test all of the logic on the R sides of the API in your package without requiring access to the remote service. Importantly, it provides three contexts that mock the network connection in different ways, as well as testing functions to assert that HTTP requests were---or were not---made. It also allows one to safely record real API responses to use as test fixtures. The ability to save responses and load them offline also enables one to write vignettes and other dynamic documents that can be distributed without access to a live server.

This package provides color palettes. They are checked for colorblind accessibility from hue, saturation, and lightness value scaling using the Chroma.js Color Palette Helper. See https://gka.github.io/palettes.

This package provides an implementation of Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic single nucleotide variants (SNVs) in circulating cell free DNA (cfDNA).

This package provides implementations of apply(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers etc. Because of its unified API, there is no need to modify any code in order to switch from sequential on the local machine to, say, distributed processing on a remote compute cluster.

This package provides procedures for fitting a principal curve to a data matrix in arbitrary dimensions.

This package provides simple, flexible assertions on data.frame or data.table objects with verbose output for vetting. While other assertion packages apply towards more general use-cases, assertable is tailored towards tabular data. It includes functions to check variable names and values, whether the dataset contains all combinations of a given set of unique identifiers, and whether it is a certain length. In addition, assertable includes utility functions to check the existence of target files and to efficiently import multiple tabular data files into one data.table.

This package provides tools to fit and predict with the high-dimensional principal fitted components model. This model is described by Cook, Forzani, and Rothman (2012) doi:10.1214/11-AOS962.

This package provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.

This package provides a generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. It can also handle clustered categorical responses.

Provide nonparametric methods for mean regression model, modal regression and conditional density estimation in the presence/absence of measurement error. Bandwidth selection is also provided for each method.

This package lets you fit pedigree-based mixed-effects models.

This package provides support for rendering of formatted text using Grid graphics. Text can be formatted via a minimal subset of Markdown, HTML, and inline CSS directives, and it can be rendered both with and without word wrap.

This package provides an efficient interface to MPI by utilizing S4 classes and methods with a focus on Single Program/Multiple Data (SPMD) parallel programming style, which is intended for batch parallel execution.

As a successor of the packages BatchJobs and BatchExperiments, this package provides a parallel implementation of the Map function for high performance computing systems managed by various schedulers. A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.

This package provides meta-analysis methods that correct for publication bias and outcome reporting bias. Four methods and a visual tool are currently included in the package.

1. The p-uniform method as described in van Assen, van Aert, and Wicherts (2015) doi:10.1037/met0000025 can be used for estimating the average effect size, testing the null hypothesis of no effect, and testing for publication bias using only the statistically significant effect sizes of primary studies.

2. The p-uniform* method as described in van Aert and van Assen (2019) doi:10.31222/osf.io/zqjr9. This method is an extension of the p-uniform method that allows for estimation of the average effect size and the between-study variance in a meta-analysis, and uses both the statistically significant and nonsignificant effect sizes.

3. The hybrid method as described in van Aert and van Assen (2017) doi:10.3758/s13428-017-0967-6. The hybrid method is a meta-analysis method for combining an original study and replication and while taking into account statistical significance of the original study. The p-uniform and hybrid method are based on the statistical theory that the distribution of p-values is uniform conditional on the population effect size.

4. The fourth method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in van Aert and van Assen (2018) doi:10.1371/journal.pone.0175302. This method computes posterior probabilities for four true effect sizes (no, small, medium, and large) based on an original study and replication while taking into account publication bias in the original study. The method can also be used for computing the required sample size of the replication akin to power analysis in null hypothesis significance testing.

The meta-plot is a visual tool for meta-analysis that provides information on the primary studies in the meta-analysis, the results of the meta-analysis, and characteristics of the research on the effect under study (van Assen and others, 2020).

Helper functions to apply the Correcting for Outcome Reporting Bias (CORB) method to correct for outcome reporting bias in a meta-analysis (van Aert & Wicherts, 2020).

This package provides functions for obtaining the density, random variates and maximum likelihood estimates of the Zero-truncated Poisson lognormal distribution and their mixture distribution.