It provides an effective, efficient, and fast way to explore the Gene Ontology (GO). Given a set of genes, the package contains functions to assess the GO and obtain the terms associated with the genes and the levels of the GO terms. The package provides functions for the three different GO ontology. We discussed the methods explicitly in the following article <doi:10.1038/s41598-020-73326-3>.

This package provides utility functions that are simply, frequently used, but may require higher performance that what can be obtained from base R. Incidentally provides support for reverse geocoding', such as matching a point with its nearest neighbour in another array. Used as a complement to package hutils by sacrificing compilation or installation time for higher running speeds. The name is a portmanteau of the author and Rcpp'.

This package provides a systematic framework for integrating multiple modalities of assays profiled on the same set of samples. The goal is to identify genes that are altered in cancer either marginally or consistently across different assays. The heterogeneity among different platforms and different samples are automatically adjusted so that the overall alteration magnitude can be accurately inferred. See Tong and Coombes (2012) <doi:10.1093/bioinformatics/bts561>.

This package provides tools for easily and flexibly creating ggplot2 maps with inset maps. One crucial feature of maps is that they have fixed coordinate ratios, i.e., they cannot be distorted, which makes it difficult to manually place inset maps. This package provides functions to automatically position inset maps based on user-defined parameters, making it extremely easy to create maps with inset maps with minimal code.

This package provides a convenience tool to create HTML with inline styles using juicyjuice and markdown packages. It is particularly useful when working on a content management system (CMS) whose code editor eliminates style and link tags. The main use case of the package is the learning management system, Moodle'. Additional helper functions for teaching purposes are provided. Learn more about juicedown at <https://kenjisato.github.io/juicedown/>.

Estimate agreement of a group of raters with a gold standard rating on a nominal scale. For a single gold standard rater the average pairwise agreement of raters with this gold standard is provided. For a group of (gold standard) raters the approach of S. Vanbelle, A. Albert (2009) <doi:10.1007/s11336-009-9116-1> is implemented. Bias and standard error are estimated via delete-1 jackknife.

Clinical reporting figures require to use consistent colors and configurations. As a part of the Roche open-source clinical reporting project, namely the NEST project, the nestcolor package specifies the color code and default theme with specifying ggplot2 theme parameters. Users can easily customize color and theme settings before using the reset of NEST packages to ensure consistent settings in both static and interactive output at the downstream.

Perform user-friendly power analyses for the random intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait autoregressive trait state (STARTS) model. The strategy as proposed by Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions include the use of parameter constraints over time, bounded estimation, generation of data with skewness and kurtosis, and the option to setup the power analysis for Mplus.

Consists of custom wrapper functions using packages openxlsx', flextable', and officer to create highly formatted MS office friendly output of your data frames. These viewer friendly outputs are intended to match expectations of professional looking presentations in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data frame to have certain properties in order to take advantage of the automated formatting.

Quick Response codes (QR codes) are a type of matrix bar code and can be used to authenticate transactions, provide access to multi-factor authentication services and enable general data transfer in an image. QR codes use four standardized encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store data. Matrix barcode generation is performed efficiently in C via the included libqrencoder library created by Kentaro Fukuchi.

This package provides a compilation of fish stock assessment methods for the analysis of length-frequency data in the context of data-poor fisheries. Includes methods and examples included in the FAO Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish stock assessment" (<https://openknowledge.fao.org/server/api/core/bitstreams/bc7c37b6-30df-49c0-b5b4-8367a872c97e/content>), as well as other more recent methods.

Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>.

This package provides significance tests for second-order stationarity for time series using bootstrap wavelet packet tests. Provides functionality to visualize the time series with the results of the hypothesis tests superimposed. The methodology is described in Cardinali, A and Nason, G P (2016) "Practical powerful wavelet packet tests for second-order stationarity." Applied and Computational Harmonic Analysis, 44, 558-585 <doi:10.1016/j.acha.2016.06.006>.

Find the location of the code for an R package based on the package's name or string representation. Checks on CRAN based on information in the URL field or BioConductor and GitHub based on constructing a URL, and verifies all paths via testing for a successful response. This can be useful when automating static code analysis based on a list of package names, and similar tasks.

Includes functions that researchers or practitioners may use to clean raw data, transferring html, xlsx, txt data file into other formats. And it also can be used to manipulate text variables, extract numeric variables from text variables and other variable cleaning processes. It is originated from a author's project which focuses on creative performance in online education environment. The resulting paper of that study will be published soon.

Data depth concept offers a variety of powerful and user friendly tools for robust exploration and inference for multivariate data. The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions.

This package provides a suite of tools to help modelers and decision-makers effectively interpret and communicate decision risk when evaluating multiple policy options. It uses model outputs from uncertainty analysis for baseline scenarios and policy alternatives to generate visual representations of uncertainty and quantitative measures for assessing associated risks. For more details see Wiggins and colleagues (2025) <doi:10.1371/journal.pone.0332522> and <https://dut.ihe.ca/>.

Randomly generate a wide range of interaction networks with specified size, average degree, modularity, and topological structure. Sample nodes and links from within simulated networks randomly, by degree, by module, or by abundance. Simulations and sampling routines are implemented in FORTRAN', providing efficient generation times even for large networks. Basic visualization methods also included. Algorithms implemented here are described in de Aguiar et al. (2017) <arXiv:1708.01242>.

This package performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <DOI:10.1093/bioinformatics/bty472>).

Applies sequential clustering algorithm to animal location data based on user-defined parameters. Plots interactive cluster maps and provides a summary dataframe with attributes for each cluster commonly used as covariates in subsequent modeling efforts. Additional functions provide individual keyhole markup language plots for quick assessment, and export of global positioning system exchange format files for navigation purposes. Methods can be found at <doi:10.1111/2041-210X.13572>.

This package provides an interface to the GenderAPI.io web service (<https://www.genderapi.io>) for determining gender from personal names, email addresses, or social media usernames. Functions are available to submit single or batch queries and retrieve additional information such as accuracy scores and country-specific gender predictions. This package simplifies integration of GenderAPI.io into R workflows for data cleaning, user profiling, and analytics tasks.

This package provides a genomic simulation approach for creating biologically informed individual genotypes from empirical data that 1) samples alleles from populations without replacement, 2) segregates alleles based on species-specific recombination rates. gscramble is a flexible simulation approach that allows users to create pedigrees of varying complexity in order to simulate admixed genotypes. Furthermore, it allows users to track haplotype blocks from the source populations through the pedigrees.

Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.

Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood and Laplace approximation model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more. See <https://samuel-watson.github.io/glmmr-web/> for a detailed manual.