Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) <doi:10.1080/03610926.2023.2251623>. This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design.

This package provides a set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz").

This package implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.

This package provides a metric expressing the quality of a UMAP layout. This is a package that contains the Saturn_coefficient() function that reads an input matrix, its dimensionality reduction produced by UMAP, and evaluates the quality of this dimensionality reduction by producing a real value in the [0; 1] interval. We call this real value Saturn coefficient. A higher value means better dimensionality reduction; a lower value means worse dimensionality reduction. Reference: Davide Chicco et al. "The Saturn coefficient for evaluating the quality of UMAP dimensionality reduction results" (2025, in preparation).

This package provides a basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.

Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid, the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve each sample can be visualized as a simple density plot, and the distance between samples can be calculated from the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences between samples can identified using a simple bootstrap procedure.

This is a R package to compute the automorphisms between pairwise aligned DNA sequences represented as elements from a Genomic Abelian group. In a general scenario, from genomic regions till the whole genomes from a given population (from any species or close related species) can be algebraically represented as a direct sum of cyclic groups or more specifically Abelian p-groups. Basically, we propose the representation of multiple sequence alignments of length N bp as element of a finite Abelian group created by the direct sum of homocyclic Abelian group of prime-power order.

Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. The top level function used to estimate marginal likelihood is called evidence(), which expects the prior name, data, and relevant prior specific parameters. This package also provides an MCMC prior sampler using the same underlying approach, implemented in prior_sampling(), which expects a prior name and prior specific parameters. Both functions also expect the number of burn-in iterations and the number of sampling iterations for the underlying MCMC sampler.

This package provides the ability to perform "Marginal Mediation"--mediation wherein the indirect and direct effects are in terms of the average marginal effects (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package. This framework allows the use of categorical mediators and outcomes with little change in interpretation from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details on the method.

Likelihood-based inference methods with doubly-truncated data are developed under various models. Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>. Parametric models from the special exponential family (SEF) are based on Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>. The parametric location-scale models are based on Dorre et al. (2021) <doi:10.1007/s00180-020-01027-6>.

The expression levels of approximately 4600 cellular RNA transcripts were assessed in CD4+ T cell lines at different times after infection with HIV-1BRU using DNA microarrays. This data corresponds to the first block of a 12 block array image (001030_08_1.GEL) in the first data set (2000095918 A) in the first experiment (CEM LAI vs HI-LAI 24hr). There are two data sets, which are part of a dye-swap experiment with replicates, representing the Cy3 (green) absorption intensities for channel 1 (hiv1raw) and the Cy5 (red) absorption intensities for channel 2 (hiv2raw).

VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor.

This package contains fast functions to calculate the exact Bayes posterior for the Sparse Normal Sequence Model, implementing the algorithms described in Van Erven and Szabo (2021, <doi:10.1214/20-BA1227>). For general hierarchical priors, sample sizes up to 10,000 are feasible within half an hour on a standard laptop. For beta-binomial spike-and-slab priors, a faster algorithm is provided, which can handle sample sizes of 100,000 in half an hour. In the implementation, special care has been taken to assure numerical stability of the methods even for such large sample sizes.

Derivation of indexes for benchmarking purposes. A methodology with flexible number of constituents is implemented. Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) <doi:10.1016/j.jempfin.2018.08.004>.

This package provides a classification (decision) tree is constructed from survival data with high-dimensional covariates. The method is a robust version of the logrank tree, where the variance is stabilized. The main function "uni.tree" returns a classification tree for a given survival dataset. The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). This tree construction algorithm is proposed by Emura et al. (2021, in review).

Allows to generate automatically testthat code files from offensive programming test cases. Generated test files are complete and ready to run. Using wyz.code.testthat you will earn a lot of time, reduce the number of errors in test case production, be able to test immediately generated files without any need to view or modify them, and enter a zero time latency between code implementation and industrial testing. As with testthat', you may complete provided test cases according to your needs to push testing further, but this need is nearly void when using wyz.code.offensiveProgramming'.

Providing a common set of simplified web scraping tools for working with the NHS Data Dictionary <https://datadictionary.nhs.uk/data_elements_overview.html>. The intended usage is to access the data elements section of the NHS Data Dictionary to access key lookups. The benefits of having it in this package are that the lookups are the live lookups on the website and will not need to be maintained. This package was commissioned by the NHS-R community <https://nhsrcommunity.com/> to provide this consistency of lookups. The OpenSafely lookups have now been added <https://www.opencodelists.org/docs/>.

Implementation of selected Tidyverse functions within DataSHIELD', an open-source federated analysis solution in R. Currently, DataSHIELD contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the clientside package which should be installed locally, and is used in conjuncture with the serverside package dsTidyverse which is installed on the remote server holding the data. For more information, see <https://www.tidyverse.org/>, <https://datashield.org/> and <https://github.com/molgenis/ds-tidyverse>.

Allows users to create time series of tropical storm exposure histories for chosen counties for a number of hazard metrics (wind, rain, distance from the storm, etc.). This package interacts with data available through the hurricaneexposuredata package, which is available in a drat repository. To access this data package, see the instructions at <https://github.com/geanders/hurricaneexposure>. The size of the hurricaneexposuredata package is approximately 20 MB. This work was supported in part by grants from the National Institute of Environmental Health Sciences (R00ES022631), the National Science Foundation (1331399), and a NASA Applied Sciences Program/Public Health Program Grant (NNX09AV81G).

When multiple Cox proportional hazard models are performed on clinical data (month or year and status) and a set of differential expressions of genes, the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. Weights are calculated using the survival p-values of genes and are utilized to calculate expression values of the signature across the selected genes in all patients in a cohort. A Single or multiple univariate or multivariate Cox proportional hazard survival analyses of the patients in one cohort can be performed by using the gene-expression signature and visualized using our survival plots.

The package provides a comprehensive mapping table of metabolites linked to Wikipathways pathways. The tables include HMDB, KEGG, ChEBI, Drugbank, PubChem compound, ChemSpider, KNApSAcK, and Wikidata IDs plus CAS and InChIKey. The tables are provided for each of the 25 species ("Anopheles gambiae", "Arabidopsis thaliana", "Bacillus subtilis", "Bos taurus", "Caenorhabditis elegans", "Canis familiaris", "Danio rerio", "Drosophila melanogaster", "Equus caballus", "Escherichia coli", "Gallus gallus", "Gibberella zeae", "Homo sapiens", "Hordeum vulgare", "Mus musculus", "Mycobacterium tuberculosis", "Oryza sativa", "Pan troglodytes", "Plasmodium falciparum", "Populus trichocarpa", "Rattus norvegicus", "Saccharomyces cerevisiae", "Solanum lycopersicum", "Sus scrofa", "Zea mays"). These table information can be used for Metabolite Set Enrichment Analysis.

Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) <doi:10.1111/2041-210X.13152>.

Generates explanations for classification and regression models and visualizes them. Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly in the model. The IME method is slower as it samples from all feature subsets. For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>, and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18). All models in package CORElearn are natively supported, for other prediction models a wrapper function is provided and illustrated for models from packages randomForest', nnet', and e1071'.

Dominance analysis is a method that allows to compare the relative importance of predictors in multiple regression models: ordinary least squares, generalized linear models, hierarchical linear models, beta regression and dynamic linear models. The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129> for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>, Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>, respectively.