Computes bounds and sensitivity parameters as part of sensitivity analysis for selection bias. Different bounds are provided: the SV (Smith and VanderWeele), sharp bounds, AF (assumption-free) bound, GAF (generalized AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity parameters for the SV, sharp, and GAF bounds assume an additional dependence structure in form of a generalized M-structure. The bounds can be calculated for any structure as long as the necessary assumptions hold. See Smith and VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>, Zetterstrom, Sjölander, and Waernabum (2025) <doi:10.1177/09622802251374168>, Zetterstrom and Waernbaum (2022) <doi:10.1515/em-2022-0108>, and Zetterstrom (2024) <doi:10.1515/em-2023-0033>.

An interactive document on the topic of basic statistical analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.

Add functionality to create drag and drop div elements in shiny.

The goal of the SwimmeR package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions. To that end SwimmeR allows results to be read in from .html sources, like Hy-Tek real time results pages, .pdf files, ISL results, Omega results, and (on a development basis) .hy3 files. Once read in, SwimmeR can convert swimming times (performances) between the computationally useful format of seconds reported to the 100ths place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community. SwimmeR can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', SCM', SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data. This is a developmental package, not yet mature.

Collision Risk Models for avian fauna (seabird and migratory birds) at offshore wind farms. The base deterministic model is derived from Band (2012) <https://tethys.pnnl.gov/publications/using-collision-risk-model-assess-bird-collision-risks-offshore-wind-farms>. This was further expanded on by Masden (2015) <doi:10.7489/1659-1> and code used here is heavily derived from this work with input from Dr A. Cook at the British Trust for Ornithology. These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, many of the species included in the stochastic collision risk models can also be found in the North Atlantic in the United States and Canada, and could be applied there.

This package provides a set of functions to build a scoring model from beginning to end, leading the user to follow an efficient and organized development process, reducing significantly the time spent on data exploration, variable selection, feature engineering, binning and model selection among other recurrent tasks. The package also incorporates monotonic and customized binning, scaling capabilities that transforms logistic coefficients into points for a better business understanding and calculates and visualizes classic performance metrics of a classification model.

This package provides functions for fitting semiparametric regression models for panel count survival data. An overview of the package can be found in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and Chiou et al. (2018) <doi:10.1111/insr.12271>.

Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).

Work with and download road traffic casualty data from Great Britain. Enables access to the UK's official road safety statistics, STATS19'. Enables users to specify a download directory for the data, which can be set permanently by adding `STATS19_DOWNLOAD_DIRECTORY=/path/to/a/dir` to your `.Renviron` file, which can be opened with `usethis::edit_r_environ()`. The data is provided as a series of `.csv` files. This package downloads, reads-in and formats the data, making it suitable for analysis. See the stats19 vignette for details. Data available from 1979 to 2024. See the official data series at <https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-accidents-safety-data>. The package is described in a paper in the Journal of Open Source Software (Lovelace et al. 2019) <doi:10.21105/joss.01181>. See Gilardi et al. (2022) <doi:10.1111/rssa.12823>, Vidal-Tortosa et al. (2021) <doi:10.1016/j.jth.2021.101291>, Tait et al. (2023) <doi:10.1016/j.aap.2022.106895>, and León et al. (2025) <doi:10.18637/jss.v114.i09> for examples of how the data can be used for methodological and empirical research.

Identifies what optimal subset of a desired number of items should be retained in a short version of a psychometric instrument to assess the â broadestâ proportion of the construct-level content of the set of items included in the original version of the said psychometric instrument. Expects a symmetric adjacency matrix as input (undirected weighted network model). Supports brute force and simulated annealing combinatorial search algorithms.

This package provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.

Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the seasonalview package. Uses the X-13-binaries from the x13binary package.

Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).

Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.

Offers Bayesian semiparametric density estimation and tail-index estimation for heavy tailed data, by using a parametric, tail-respecting transformation of the data to the unit interval and then modeling the transformed data with a purely nonparametric logistic Gaussian process density prior. Based on Tokdar et al. (2022) <doi:10.1080/01621459.2022.2104727>.

This package provides a compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) <doi:10.1071/CP16444>); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) <doi:10.2136/sssaj1971.03615995003500040048x>), 4) the linear-plateau regression (Anderson & Nelson (1975) <doi:10.2307/2529422>), 5) the quadratic-plateau regression (Bullock & Bullock (1994) <doi:10.2134/agronj1994.00021962008600010033x>), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) <doi:10.2134/asaspecpub29.c1>). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects.

In ecology, spatial data is often represented using polygons. These polygons can represent a variety of spatial entities, such as ecological patches, animal home ranges, or gaps in the forest canopy. Researchers often need to determine if two spatial processes, represented by these polygons, are independent of each other. For instance, they might want to test if the home range of a particular animal species is influenced by the presence of a certain type of vegetation. To address this, Godoy et al. (2022) (<doi:10.1016/j.spasta.2022.100695>) developed conditional Monte Carlo tests. These tests are designed to assess spatial independence while taking into account the shape and size of the polygons.

Empirical likelihood methods for asymptotically efficient estimation of models based on conditional or unconditional moment restrictions; see Kitamura, Tripathi & Ahn (2004) <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013) <doi:10.1002/cjs.11183>. Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in Rcpp for speed.

When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.

This package provides a subgroup identification method for precision medicine based on quantitative objectives. This method can handle continuous, binary and survival endpoint for both prognostic and predictive case. For the predictive case, the method aims at identifying a subgroup for which treatment is better than control by at least a pre-specified or auto-selected constant. For the prognostic case, the method aims at identifying a subgroup that is at least better than a pre-specified/auto-selected constant. The derived signature is a linear combination of predictors, and the selected subgroup are subjects with the signature > 0. The false discover rate when no true subgroup exists is controlled at a user-specified level.

This package implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.

Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.

This package provides the SMOTE with Boosting (SMOTEWB) algorithm. See F. SaÄ lam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. It is a SMOTE-based resampling technique which creates synthetic data on the links between nearest neighbors. SMOTEWB uses boosting weights to determine where to generate new samples and automatically decides the number of neighbors for each sample. It is robust to noise and outperforms most of the alternatives according to Matthew Correlation Coefficient metric. Alternative resampling methods are also available in the package.

Presidential Election data of "Sri Lanka"" is stored in Pdf files, through Pdf scraping they are converted into data-frames and stored in this R package.