This package provides a collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. Air quality data are sourced from open and publicly accessible repositories and can be found in these locations: <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> and <https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm>. The web server space has been provided by Ricardo Energy & Environment.

Generates binary test data based on Item Response Theory using the two-parameter logistic model (Lord, 1980 <doi:10.4324/9780203056615>). Useful functions for test equating are included, e.g. functions for generating internal and external common items between test forms and a function to create a linkage plans between those forms. Ancillary functions for generating true item and person parameters as well as for calculating the probability of a person correctly answering an item are also included.

Implementation of all possible forms of 2x2 and 3x3 space-filling curves, i.e., the generalized forms of the Hilbert curve <https://en.wikipedia.org/wiki/Hilbert_curve>, the Peano curve <https://en.wikipedia.org/wiki/Peano_curve> and the Peano curve in the meander type (Figure 5 in <https://eudml.org/doc/141086>). It can generates nxn curves expanded from any specific level-1 units. It also implements the H-curve and the three-dimensional Hilbert curve.

Spatial model calculation for static and dynamic panel data models, weights matrix creation and Bayesian model comparison. Bayesian model comparison methods were described by LeSage (2014) <doi:10.1016/j.spasta.2014.02.002>. The Lee'-'Yu transformation approach is described in Yu', De Jong and Lee (2008) <doi:10.1016/j.jeconom.2008.08.002>, Lee and Yu (2010) <doi:10.1016/j.jeconom.2009.08.001> and Lee and Yu (2010) <doi:10.1017/S0266466609100099>.

Calculate solar potential for LiDAR point clouds using the VOSTOK (Voxel Octree Solar Toolkit) algorithm. This R program provides an interface to the original VOSTOK C++ implementation by Bechtold and Hofle (2020), enabling efficient ray casting and solar position algorithms to compute solar irradiance for each point while accounting for shadowing effects. Integrates seamlessly with the lidR package for LiDAR data processing workflows. The original VOSTOK toolkit is available at <doi:10.11588/data/QNA02B>.

The concept of reliable and clinically significant change (Jacobson & Truax, 1991) helps you answer the following questions for a sample with two measurements at different points in time (pre & post): Which proportion of my sample has a (considering the reliability of the instrument) probably not-just-by-chance difference in pre- vs. post-scores? Which proportion of my sample does not only change in a statistically significant way (see question one), but also in a clinically significant way (e.g. change from a test score regarded "dysfunctional" to a score regarded "functional")? This package allows you to very easily create a scatterplot of your sample in which the x-axis maps to the pre-scores, the y-axis maps to the post-scores and several graphical elements (lines, colors) allow you to gain a quick overview about reliable changes in these scores. An example of this kind of plot is Figure 2 of Jacobson & Truax (1991). Referenced article: Jacobson, N. S., & Truax, P. (1991) <doi:10.1037/0022-006X.59.1.12>.

Guile RDF is an implementation of the RDF (Resource Description Framework) format defined by the W3C for GNU Guile. RDF structures include triples (facts with a subject, a predicate and an object), graphs which are sets of triples, and datasets, which are collections of graphs.

RDF specifications include the specification of concrete syntaxes and of operations on graphs. This library implements some basic functionalities, such as parsing and producing turtle and nquads syntax, as well as manipulating graphs and datasets.

This package provides whole-genome mappability tracks on human hg19/hg38 assembly. We employed the 100-mers mappability track from the ENCODE Project and computed weighted average of the mappability scores if multiple ENCODE regions overlap with the same bin. “Blacklist” bins, including segmental duplication regions and gaps in reference assembly from telomere, centromere, and/or heterochromatin regions are included. The dataset consists of three assembled .bam files of single-cell whole genome sequencing from 10X for illustration purposes.

This package provides a collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established.

Time series analysis of network connectivity. Detects and visualizes change points between networks. Methods included in the package are discussed in depth in Baek, C., Gates, K. M., Leinwand, B., Pipiras, V. (2021) "Two sample tests for high-dimensional auto-covariances" <doi:10.1016/j.csda.2020.107067> and Baek, C., Gampe, M., Leinwand B., Lindquist K., Hopfinger J. and Gates K. (2023) â Detecting functional connectivity changes in fMRI dataâ <doi:10.1007/s11336-023-09908-7>.

This package provides functions to test for gene x gene interactions in a bi-parental population of inbred lines. The data are fitted with the mixed linear model described in Rio et al. (2022) <doi:10.1101/2022.12.18.520958>, that accounts for gene x gene interactions at both the fixed effect and variance levels. The package also provides graphical tools to display the gene x gene interaction trend at the mean level and the variance component analysis.

This package provides a ggplot2 extension providing an integrative framework for composable visualization, enabling the creation of complex multi-plot layouts such as insets, circular arrangements, and multi-panel compositions. Built on the grammar of graphics, it offers tools to align, stack, and nest plots, simplifying the construction of richly annotated figures for high-dimensional data contextsâ such as genomics, transcriptomics, and microbiome studiesâ by making it easy to link related plots, overlay clustering results, or highlight shared patterns.

Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.

Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.

R interface for the netstat command line utility used to retrieve and parse commonly used network statistics, including available and in-use transmission control protocol (TCP) ports. Primers offering technical background information on the netstat command line utility are available in the "Linux System Administrator's Manual" by Michael Kerrisk (2014) <https://man7.org/linux/man-pages/man8/netstat.8.html>, and on the Microsoft website (2017) <https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/netstat>.

An R-package-version of an open online science-based personality test from <https://openpsychometrics.org/tests/IPIP-BFFM/>, providing a better-designed interface and a more detailed report. The core command launch_test() opens a personality test in your browser, and generates a report after you click "Submit". In this report, your results are compared with other people's, to show what these results mean. Other people's data is from <https://openpsychometrics.org/_rawdata/BIG5.zip>.

The Common Workflow Language <https://www.commonwl.org/> is an open standard for describing data analysis workflows. This package takes the raw Common Workflow Language workflows encoded in JSON or YAML and turns the workflow elements into tidy data frames or lists. A graph representation for the workflow can be constructed and visualized with the parsed workflow inputs, outputs, and steps. Users can embed the visualizations in their Shiny applications, and export them as HTML files or static images.

Simple trustworthy utility functions to use TauDEM (Terrain Analysis Using Digital Elevation Models <https://hydrology.usu.edu/taudem/taudem5/>) command-line interface. This package provides a guide to installation of TauDEM and its dependencies GDAL (Geopatial Data Abstraction Library) and MPI (Message Passing Interface) for different operating systems. Moreover, it checks that TauDEM and its dependencies are correctly installed and included to the PATH, and it provides wrapper commands for calling TauDEM methods from R.

Speech-to-text transcription using a native R torch implementation of OpenAI Whisper model <https://github.com/openai/whisper>. Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from HuggingFace <https://huggingface.co/> via the hfhub package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the torch package.

C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, mpi (for parallel computing), qt (showing how to embed RInside inside a Qt GUI application), wt (showing how to build a "web-application" using the Wt toolkit), armadillo (for RInside use with RcppArmadillo'), eigen (for RInside use with RcppEigen'), and c_interface for a basic C interface and Ruby illustration. The examples use GNUmakefile(s) with GNU extensions, so a GNU make is required (and will use the GNUmakefile automatically). Doxygen'-generated documentation of the C++ classes is available at the RInside website as well.

Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) <doi:10.1002/sim.2496>; Faraggi, D. (2003) <doi:10.1111/1467-9884.00350>; Gu, J. et al. (2008) <doi:10.1002/sim.3366>; Inacio de Carvalho, V. et al. (2013) <doi:10.1214/13-BA825>; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2022) <doi:10.1214/21-STS839>; Janes, H., and Pepe, M.S. (2009) <doi:10.1093/biomet/asp002>; Pepe, M.S. (1998) <http://www.jstor.org/stable/2534001?seq=1>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1016/j.csda.2010.07.018>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1007/s11222-010-9184-1>. Please see Rodriguez-Alvarez, M.X. and Inacio, V. (2021) <doi:10.32614/RJ-2021-066> for more details.

MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features.

Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples.

Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.