An HTTP API client for Lemmy (<https://github.com/LemmyNet/lemmy>) in R. Code and documentation are generated from the official JavaScript client source (<https://github.com/LemmyNet/lemmy-js-client>).

This package implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) <doi:10.19173/irrodl.v18i2.2952>. Automates the process of identifying learning materials that are not effectively supporting student learning in technology-mediated courses by synthesizing information about access to course content and performance on assessments.

This package contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (<https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html>) and NCEP/DOE Reanalysis II (<https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html>) datasets. Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time. We also provide functions to visualize these weather data on a map. There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.

Datasets and utility functions to support the book "R for Plant Disease Epidemiology" (R4PDE). It includes functions for quantifying disease, assessing spatial patterns, and modeling plant disease epidemics based on weather predictors. These tools are intended for teaching and research in plant disease epidemiology. Several functions are based on classical and contemporary methods, including those discussed in Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>.

Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in remverse and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>).

This package provides functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.

This package provides functions to load and manage data from Apple Ads accounts using the Apple Ads Campaign Management API <https://developer.apple.com/documentation/apple_ads>.

Additional matrix functionality for R including: (1) wrappers for the base matrix function that allow matrices to be created from character strings and lists (the former is especially useful for creating block matrices), (2) better printing of large matrices via the generic "pretty" print function, and (3) a number of convenience functions for users more familiar with other scientific languages like Julia', Matlab'/'Octave', or Python'+'NumPy'.

As of RStudio v1.3, the preferences in the Global Options dialog (and a number of other preferences that arenâ t) are now saved in simple, plain-text JSON files. This package provides an interface for working with these RStudio JSON preference files to easily make modifications without using the point-and-click option menus. This is particularly helpful when working on teams to ensure a unified experience across machines and utilizing settings for best practices.

This package provides tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.

This package provides a tool for processing Articulate Assistant Advancedâ ¢ (AAA) ultrasound tongue imaging data and Carstens AG500/1 electro-magnetic articulographic data.

Plot rpart models. Extends plot.rpart() and text.rpart() in the rpart package.

New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested and their performance accurately assessed. This requires densities that offer challenging properties to the novel sampling algorithms. One such popular problem is the Rosenbrock function. However, while its shape lends itself well to a benchmark problem, no codified multivariate expansion of the density exists. We have developed an extension to this class of distributions and supplied densities and direct sampler functions to assess the performance of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <arXiv:1903.09556>.

This package contains three functions that access environmental data from any ERDDAPâ ¢ data web service. The rxtracto() function extracts data along a trajectory for a given "radius" around the point. The rxtracto_3D() function extracts data in a box. The rxtractogon() function extracts data in a polygon. All of those three function use the rerddap package to extract the data, and should work with any ERDDAPâ ¢ server. There are also two functions, plotBBox() and plotTrack() that use the plotdap package to simplify the creation of maps of the data.

Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.

Collection of functions for fitting distributions to given data or by known quantiles. Two main functions fit.perc() and fit.cont() provide users a GUI that allows to choose a most appropriate distribution without any knowledge of the R syntax. Note, this package is a part of the rrisk project.

This package provides color schemes for maps and other graphics designed by CARTO as described at <https://carto.com/carto-colors/>. It includes four types of palettes: aggregation, diverging, qualitative, and quantitative.

This package provides a straightforward model to estimate soil migration rates across various soil contexts. Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) <doi:10.1016/j.jaridenv.2004.02.003> and Soto and Navas (2008) <doi:10.1016/j.radmeas.2008.02.024>. RadEro provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: <https://github.com/eead-csic-eesa/RadEro>.

Wrapper for the PoetryDB API <http://poetrydb.org> that allows for interaction and data extraction from the database in an R interface. The PoetryDB API is a database of poetry and poets implemented with MongoDB to enable developers and poets to easily access one of the most comprehensive poetry databases currently available.

Distance-sampling (<doi:10.1007/978-3-319-19219-2>) is a field survey and analytical method that estimates density and abundance of survey targets (e.g., animals) when detection probability declines with observation distance. Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Analysis involves fitting smooth (parametric) curves to histograms of observation distances and using those functions to adjust density estimates for missed targets. Routines included here fit curves to observation distance histograms, estimate effective sampling area, density of targets in surveyed areas, and the abundance of targets in a surrounding study area. Confidence interval estimation uses built-in bootstrap resampling. Help files are extensive and have been vetted by multiple authors. Many tutorials are available on the package's website (URL below).

Non-parametric clustering of joint pattern multi-genetic/epigenetic factors. This package contains functions designed to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), gene expression (GE), and covariate data. The novel concept follows the general K-means (Hartigan and Wong (1979) <doi:10.2307/2346830> framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions.

Allows loading and displaying an Observable notebook (online JavaScript notebooks powered by <https://observablehq.com>) as an HTML Widget in an R session, shiny application or rmarkdown document.

We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model.

Rcpp bindings for PLANC', a highly parallel and extensible NMF/NTF (Non-negative Matrix/Tensor Factorization) library. Wraps algorithms described in Kannan et. al (2018) <doi:10.1109/TKDE.2017.2767592> and Eswar et. al (2021) <doi:10.1145/3432185>. Implements algorithms described in Welch et al. (2019) <doi:10.1016/j.cell.2019.05.006>, Gao et al. (2021) <doi:10.1038/s41587-021-00867-x>, and Kriebel & Welch (2022) <doi:10.1038/s41467-022-28431-4>.