xwayland-run contains a set of small utilities revolving around running Xwayland and various Wayland compositor headless, namely:
xwayland-run: Spawn X11 client within its own dedicatedXwaylandrootful instance.wlheadless-run: Run Wayland client on a set of supported Wayland headless compositors.xwfb-run: Combination of above two tools to be used as a direct replacement forxvfb-runspecifically.
Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.
Manage dependencies during package development. This can retrieve all dependencies that are used in ".R" files in the "R/" directory, in ".Rmd" files in "vignettes/" directory and in roxygen2 documentation of functions. There is a function to update the "DESCRIPTION" file of your package with CRAN packages or any other remote package. All functions to retrieve dependencies of ".R" scripts and ".Rmd" or ".qmd" files can be used independently of a package development.
Run other estimation and simulation software via the nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>) interface including PKNCA', NONMEM and Monolix'. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be run directly instead of setting up command line usage.
An implementation of the bridge distribution with logit-link in R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate bridge distribution was derived as the distribution of the random intercept that bridged a marginal logistic regression and a conditional logistic regression. The conditional and marginal regression coefficients are a scalar multiple of each other. Such is not the case if the random intercept distribution was Gaussian.
Data recorded as paths or trajectories may be suitably described by curves, which are independent of their parametrization. For the space of such curves, the package provides functionalities for reading curves, sampling points on curves, calculating distance between curves and for computing Tukey curve depth of a curve w.r.t. to a bundle of curves. For details see Lafaye De Micheaux, Mozharovskyi, and Vimond (2021) <doi:10.48550/arXiv.1901.00180>.
This package provides functions for the calculation and plotting of synchrony in tree growth from tree-ring width chronologies (TRW index). It combines variance-covariance (VCOV) mixed modelling with functions that quantify the degree to which the TRW chronologies contain a common temporal signal. It also implements temporal trends in spatial synchrony using a moving window. These methods can also be used with other kind of ecological variables that have temporal autocorrelation corrected.
Estimates the Dyad Ratios Algorithm for pooling and smoothing poll estimates. The Dyad Ratios Algorithm smooths both forward and backward in time over polling results allowing differences in both question type and polling house. The result is an estimate of a single latent variable that describes the systematic trend over time in the (noisy) polling results. See James A. Stimson (2018) <doi:10.1177/0759106318761614> and the package's vignette for more details.
Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <arXiv:1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.
This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.
Data sets from a variety of biological sample matrices, analysed using a number of mass spectrometry based metabolomic analytical techniques. The example data sets are stored remotely using GitHub releases <https://github.com/aberHRML/metaboData/releases> which can be accessed from R using the package. The package also includes the abr1 FIE-MS data set from the FIEmspro package <https://users.aber.ac.uk/jhd/> <doi:10.1038/nprot.2007.511>.
Calculate ocean wave height summary statistics and process data from bottom-mounted pressure sensor data loggers. Derived primarily from MATLAB functions provided by U. Neumeier at <http://neumeier.perso.ch/matlab/waves.html>. Wave number calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) "Direct Solution of Wave Dispersion Equation", American Society of Civil Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, Vol 105, pp 457-459.
Validate data in data frames, tibble objects, Spark DataFrames', and database tables. Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions. Many other workflows are available including an information management workflow, where the aim is to record, collect, and generate useful information on data tables.
Phenotypic analysis of data coming from high throughput phenotyping (HTP) platforms, including different types of outlier detection, spatial analysis, and parameter estimation. The package is being developed within the EPPN2020 project (<https://cordis.europa.eu/project/id/731013>). Some functions have been created to be used in conjunction with the R package asreml for the ASReml software, which can be obtained upon purchase from VSN international (<https://vsni.co.uk/software/asreml-r/>).
Basic4Cseq is an R package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile.
This package provides a complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.
The goal of BayesPower is to provide tools for Bayesian sample size determination and power analysis across a range of common hypothesis testing scenarios using Bayes factors. The main function, BayesPower_BayesFactor(), launches an interactive shiny application for performing these analyses. The application also provides command-line code for reproducibility. Details of the methods are described in the tutorial by Wong, Pawel, and Tendeiro (2025) <doi:10.31234/osf.io/pgdac_v2>.
This package provides tools to create, validate, and export BioCompute Objects described in King et al. (2019) <doi:10.17605/osf.io/h59uh>. Users can encode information in data frames, and compose BioCompute Objects from the domains defined by the standard. A checksum validator and a JSON schema validator are provided. This package also supports exporting BioCompute Objects as JSON, PDF, HTML, or Word documents, and exporting to cloud-based platforms.
Represents generalized geometric ellipsoids with the "(U,D)" representation. It allows degenerate and/or unbounded ellipsoids, together with methods for linear and duality transformations, and for plotting. Thus ellipsoids are naturally extended to include lines, hyperplanes, points, cylinders, etc. This permits exploration of a variety to statistical issues that can be visualized using ellipsoids as discussed by Friendly, Fox & Monette (2013), Elliptical Insights: Understanding Statistical Methods Through Elliptical Geometry <doi:10.1214/12-STS402>.
Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.
Integration of the units and errors packages for a complete quantity calculus system for R vectors, matrices and arrays, with automatic propagation, conversion, derivation and simplification of magnitudes and uncertainties. Documentation about units and errors is provided in the papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in those packages as vignettes; see citation("quantities") for details.
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].
The goal of siteymlgen is to make it easy to organise the building of your R Markdown website. The init() function placed within the first code chunk of the index.Rmd file of an R project directory will initiate the generation of an automatically written _site.yml file. siteymlgen recommends a specific naming convention for your R Markdown files. This naming will ensure that your navbar layout is ordered according to a hierarchy.
Themes for ggplot2 are a convenient way to style plots. The hrbrthemes package contains a particularly nice one, but brings along a significant tail of dependencies. So this (currently experimental) package brings along just the theme_ipsum_rc theme using the Roboto Condensed font. Should the font not be installed on your system, see the help in the package hrbrthemes on how to install Roboto Condensed'. Note that hrbrthemes is now archived at CRAN.