Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions.

It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. It generates individual insurance claims including open claims, reopened claims, incurred but not reported claims and future claims. It also includes claim data fitting functions to help set simulation assumptions. It is useful for claim level reserving analysis. Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.

This package provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.

This package creates auto-grading check-fields and check-boxes for rmarkdown or quarto HTML. It can be used in class, when teacher share materials and tasks, so students can solve some problems and check their work. In contrast to the learnr package, the checkdown package works serverlessly without shiny'.

Hardware-based support for CRC32C cyclic redundancy checksum function is made available for x86_64 systems with SSE2 support as well as for arm64', and detected at build-time via cmake with a software-based fallback. This functionality is exported at the C'-language level for use by other packages. CRC32C is described in RFC 3270 at <https://datatracker.ietf.org/doc/html/rfc3720> and is based on Castagnoli et al <doi:10.1109/26.231911>.

This package implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.

This package provides a candidate correspondence table between two classifications can be created when there are correspondence tables leading from the first classification to the second one via intermediate pivot classifications. The correspondence table between two statistical classifications can be updated when one of the classifications gets updated to a new version.

Although many software tools can perform meta-analyses on genetic case-control data, none of these apply to combined case-control and family-based (TDT) studies. This package conducts fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986) <DOI:10.1016/0197-2456(86)90046-2>] meta-analyses on combined genetic data. Specifically, this package implements a fixed-effects model [Kazeem and Farrall (2005) <DOI:10.1046/j.1529-8817.2005.00156.x>] and a random-effects model [Nicodemus (2008) <DOI:10.1186/1471-2105-9-130>] for combined studies.

Create cumulative odds ratio plot to visually inspect the proportional odds assumption from the proportional odds model.

Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination.

Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.

Clustering multi-subject resting state functional Magnetic Resonance Imaging data. This methods enables the clustering of subjects based on multi-subject resting state functional Magnetic Resonance Imaging data. Objects are clustered based on similarities and differences in cluster-specific estimated components obtained by Independent Component Analysis.

This package provides ability to control how many times in function calls conditions are thrown (shown to the user). Includes control of warnings and messages.

Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.

Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials.

This package provides a tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) <doi:10.48550/arXiv.2505.20168>. Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the metafor package <https://CRAN.R-project.org/package=metafor>.

This package provides a tiny package to generate CRediT author statements (<https://credit.niso.org/>). It provides three functions: create a template, read it back and generate the CRediT author statement in a text file.

Fast fitting of Stable Isotope Mixing Models in R. Allows for the inclusion of covariates. Also has built-in summary functions and plot functions which allow for the creation of isospace plots. Variational Bayes is used to fit these models, methods as described in: Tran et al., (2021) <doi:10.48550/arXiv.2103.01327>.

This package implements a methodology for using cell volume distributions to estimate cell growth rates and division times that is described in the paper, "Cell Volume Distributions Reveal Cell Growth Rates and Division Times", by Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro Tona and Anne L. Plant, which appeared in the Journal of Theoretical Biology. In order to reproduce the analysis used to obtain Table 1 in the paper, execute the command "example(fitVolDist)".

Expands the connector <https://github.com/NovoNordisk-OpenSource/connector> package and provides a convenient interface for accessing and interacting with Databricks <https://www.databricks.com> volumes and tables directly from R.

Encode and decode c-squares, from and to simple feature (sf) or spatiotemporal arrays (stars) objects. Use c-squares codes to quickly join or query spatial data.

Maximum likelihood estimation of the Cauchy-Cacoullos (discrete Cauchy) distribution. Probability mass, distribution and quantile function are also included. The reference paper is: Papadatos N. (2022). "The Characteristic Function of the Discrete Cauchy Distribution in Memory of T. Cacoullos". Journal of Statistical Theory Practice, 16(3): 47. <doi:10.1007/s42519-022-00268-6>.

Canonical correlation analysis and maximum correlation via projection pursuit, as well as fast implementations of correlation estimators, with a focus on robust and nonparametric methods.

This package provides a method for pattern discovery in weighted graphs as outlined in Thistlethwaite et al. (2021) <doi:10.1371/journal.pcbi.1008550>. Two use cases are achieved: 1) Given a weighted graph and a subset of its nodes, do the nodes show significant connectedness? 2) Given a weighted graph and two subsets of its nodes, are the subsets close neighbors or distant?