This package implements statistical tools for analyzing, simulating, and computing properties of the New Topp-Leone Kumaraswamy Inverse Exponential (NTLKwIEx) distribution. See Atchadé M, Otodji T, and Djibril A (2024) <doi:10.1063/5.0179458> and Atchadé M, Otodji T, Djibril A, and N'bouké M (2023) <doi:10.1515/phys-2023-0151> for details.

This package provides a sigmoidal quantile function estimator based on a newly defined generalized expectile function. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. The package is based on the method introduced in Hutson (2024) <doi:10.1080/03610918.2022.2032161>.

Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the irwsva.build function of the sva package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.

This is a collection of functions to calculate stop-signal reaction time (SSRT). Includes functions for both "integration" and "mean" methods; both fixed and adaptive stop-signal delays are supported (see appropriate functions). Calculation is based on Verbruggen et al. (2019) <doi:10.7554/eLife.46323.001> and Verbruggen et al. (2013) <doi:10.1177/0956797612457390>.

This package implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.

Calculate the win ratio for prioritized outcomes and the 95% confidence interval based on Bebu and Lachin (2016) <doi:10.1093/biostatistics/kxv032>. Three type of outcomes can be analyzed: survival "failure-time" events, repeated survival "failure-time" events and continuous or ordinal "non-failure time" events that are captured at specific time-points in the study.

This package provides a package to merge, filter sort, organise and otherwise mash together metabolite annotation tables. Metabolite annotations can be imported from multiple sources (software) and combined using workflow steps based on S4 class templates derived from the `struct` package. Other modular workflow steps such as filtering, merging, splitting, normalisation and rest-api queries are included.

This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.

This package provides tools to import transcript-level abundance, estimated counts and transcript lengths, and to summarize them into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.

This package aims to bring the power and flexibility of AnnData to the R ecosystem, allowing you to effortlessly manipulate and analyze your single-cell data. This package lets you work with backed h5ad and zarr files, directly access various slots (e.g. X, obs, var), or convert the data into SingleCellExperiment and Seurat objects.

This package provides flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. There are also tools for fitting and predicting from fully parametric multi-state models.

Named after the Irish name for weather, this package contains tidied data from the Irish Meteorological Service's hourly observations for 2017. In all, the data sets include observations from 25 weather stations, and also latitude and longitude coordinates for each weather station. Now includes energy generation data for Ireland and Northern Ireland (2017), including Wind Generation data.

Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (<https://osf.io/jxnsv>). Besides generic functions, the package also provides an intuitive Shiny application, that can be run in local R environments.

This package provides function to create, read, write, and work with iCalendar files (which typically have .ics or .ical extensions), and the scheduling data, calendars and timelines of people, organisations and other entities that they represent. iCalendar is an open standard for exchanging calendar and scheduling information between users and computers, described at <https://icalendar.org/>.

Doubly robust methods for evaluating surrogate markers as outlined in: Agniel D, Hejblum BP, Thiebaut R & Parast L (2022). "Doubly robust evaluation of high-dimensional surrogate markers", Biostatistics <doi:10.1093/biostatistics/kxac020>. You can use these methods to determine how much of the overall treatment effect is explained by a (possibly high-dimensional) set of surrogate markers.

This package provides a set of fast tools for converting a textual corpus into a set of normalized tables. Users may make use of the udpipe back end with no external dependencies, or a Python back ends with spaCy <https://spacy.io>. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, and dependency parsing.

Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

Experiences studies are an integral component of the actuarial control cycle. Regardless of the decrement or policyholder behavior of interest, the analyses conducted is often the same. Ultimately, this package aims to reduce time spent writing the same code used for different experience studies, therefore increasing the time for to uncover new insights inherit within the relevant experience.

This package provides tools for exploratory analysis of tabular data using colour highlighting. Highlighting is displayed in any console supporting ANSI colours, and can be converted to HTML', typst', latex and SVG'. quarto and rmarkdown rendering are directly supported. It is also possible to add colour to regular expression matches and highlight differences between two arbitrary R objects.

This package provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. <doi:10.18637/jss.v098.i10>.

Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate GENEPOP format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.

An easy way to conduct flexible scan. Monte-Carlo method is used to test the spatial clusters given the cases, population, and shapefile. A table with formal style and a map with clusters are included in the result report. The method can be referenced at: Toshiro Tango and Kunihiko Takahashi (2005) <doi:10.1186/1476-072X-4-11>.

The forensIT package is a comprehensive statistical toolkit tailored for handling missing person cases. By leveraging information theory metrics, it enables accurate assessment of kinship, particularly when limited genetic evidence is available. With a focus on optimizing statistical power, forensIT empowers investigators to effectively prioritize family members, enhancing the reliability and efficiency of missing person investigations.

Inference, goodness-of-fit tests, and predictions for continuous and discrete univariate Hidden Markov Models (HMM), including zero-inflated distributions. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Nasri et al (2020) <doi:10.1029/2019WR025122>.