This package performs the random projection test (Lopes et al., (2011) <doi:10.48550/arXiv.1108.2401>) for the one-sample and two-sample hypothesis testing problem for equality of means in the high dimensional setting. We are interested in detecting the mean vector in the one-sample problem or the difference between mean vectors in the two-sample problem.

Empirical best linear unbiased prediction (EBLUP) and robust prediction of the area-level means under the basic unit-level model. The model can be fitted by maximum likelihood or a (robust) M-estimator. Mean square prediction error is computed by a parametric bootstrap.

Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.

The R Formatter formats R source code. It is very much based on formatR, but tries to improve it by heuristics. For example, spaces can be forced around the division operator "/".

Allow access to both public and private end points to Coinbase Pro (erstwhile GDAX) cryptocurrency exchange. For authenticated flow, users must have valid api, secret and passphrase to be able to connect.

Many packages in the r-dcm family take similar arguments, which are checked for expected structures and values. Rather than duplicating code across several packages, commonly used check functions are included here. This package can then be imported to access the check functions in other packages.

This package provides methods for ranking responses of a single response question or a multiple response question are described in the two papers: 1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of Applied Statistics, 35, 465-474. <DOI:10.1080/02664760801924533> 2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple Response Questions. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177, 191-208. <DOI:10.1111/rssa.12009>.

Minimal and lightweight configuration tool that provides basic support for YAML configuration files without requiring additional package dependencies. It offers a simple method for loading and parsing configuration settings, making it ideal for quick prototypes and lightweight projects.

Create and combine HTML and PDF reports from within R. Possibility to design tables and listings for reporting and also include R plots.

This package provides a computational resource designed to accurately detect microbial nucleic acids while filtering out contaminants and false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. For more details, see Ghaddar (2023) <doi:10.1038/s43588-023-00507-1>. This implementation leverages the polars package for fast and systematic microbial signal recovery and denoising from host tissue genomic sequencing.

Enhances the R Optimization Infrastructure ('ROI') package with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.

This package provides a tool to calculate Cardiovascular Risk Scores in large data frames as published in Perez-Vicencio, et al (2024) <doi:10.1136/openhrt-2024-002755>. Cardiovascular risk scores are statistical tools used to assess an individual's likelihood of developing a cardiovascular disease based on various risk factors, such as age, gender, blood pressure, cholesterol levels, and smoking. Here we bring together the six most commonly used in the emergency department. Using RiskScorescvd', you can calculate all the risk scores in an extended dataset in seconds. PCE (ASCVD) described in Goff, et al (2013) <doi:10.1161/01.cir.0000437741.48606.98>. EDACS described in Mark DG, et al (2016) <doi:10.1016/j.jacc.2017.11.064>. GRACE described in Fox KA, et al (2006) <doi:10.1136/bmj.38985.646481.55>. HEART is described in Mahler SA, et al (2017) <doi:10.1016/j.clinbiochem.2017.01.003>. SCORE2/OP described in SCORE2 working group and ESC Cardiovascular risk collaboration (2021) <doi:10.1093/eurheartj/ehab309>. TIMI described in Antman EM, et al (2000) <doi:10.1001/jama.284.7.835>. SCORE2-Diabetes described in SCORE2-Diabetes working group and ESC Cardiovascular risk collaboration (2023) <doi:10.1093/eurheartj/ehab260>. SCORE2/OP with CKD add-on described in Kunihiro M et al (2022) <doi:10.1093/eurjpc/zwac176>.

This package provides tools to help with shiny reactivity. The react object offers an alternative way to call reactive expressions to better identify them in the server code.

Collect your data on digital marketing campaigns from reddit Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

Generate random positions (latitude/longitude), Well-known text ('WKT') points or polygons, or GeoJSON points or polygons.

Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the igraph package of Csárdi, et al. (2006 <doi:10.5281/zenodo.7682609>).

This package provides a machine learning package for automatic text classification that makes it simple for novice users to get started with machine learning, while allowing experienced users to easily experiment with different settings and algorithm combinations. The package includes eight algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks), comprehensive analytics, and thorough documentation.

An integrated package for constructing random forest prediction intervals using a fast implementation package ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.

This package provides a trimmed down copy of the "kent-core source tree" turned into a C library for manipulation of .2bit files. See <https://genome.ucsc.edu/FAQ/FAQformat.html#format7> for a quick overview of the 2bit format. The "kent-core source tree" can be found here: <https://github.com/ucscGenomeBrowser/kent-core/>. Only the .c and .h files from the source tree that are related to manipulation of .2bit files were kept. Note that the package is primarily useful to developers of other R packages who wish to use the 2bit C library in their own C'/'C++ code.

This package provides functions for semi-automated quality control of bulk RNA-seq data.

Easy-to-use functions for downloading air quality data from the Mexican National Air Quality Information System (SINAICA). Allows you to query pollution and meteorological parameters from more than a hundred monitoring stations located throughout Mexico. See <https://sinaica.inecc.gob.mx> for more information.

Recursive algorithms for computing various relatedness coefficients, including pairwise kinship, kappa and identity coefficients. Both autosomal and X-linked coefficients are computed. Founders are allowed to be inbred, which enables construction of any given kappa coefficients, as described in Vigeland (2020) <doi:10.1007/s00285-020-01505-x>. In addition to the standard coefficients, ribd also computes a range of lesser-known coefficients, including generalised kinship coefficients, multi-person coefficients and two-locus coefficients (Vigeland, 2023, <doi:10.1093/g3journal/jkac326>). Many features of ribd are available through the online app QuickPed at <https://magnusdv.shinyapps.io/quickped>; see Vigeland (2022) <doi:10.1186/s12859-022-04759-y>.

Visualization platform for T cell receptor repertoire analysis output results. It includes comparison of sequence frequency among samples, network of similar sequences and convergent recombination source between species. Currently repertoire analysis is in early stage of development and requires new approaches for repertoire data examination and assessment as we intend to develop. No publication is available yet (will be available in the near future), Efroni (2021) <https:>.

Invoke a BUGS model in OpenBUGS or WinBUGS', a class "bugs" for BUGS results and functions to work with that class. Function write.model() allows a BUGS model file to be written. The class and auxiliary functions could be used with other MCMC programs, including JAGS'. The suggested package BRugs (only needed for function openbugs()) is only available from the CRAN archives, see <https://cran.r-project.org/package=BRugs>.