This package provides a simple package to grab cheat sheets and save them to your local computer.

This package provides functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) <doi:10.1016/j.wace.2018.01.002>.

This package provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.

This is an open-source implementation of the Congruent Matching Profile Segments (CMPS) method (Chen et al. 2019)<doi:10.1016/j.forsciint.2019.109964>. In general, it can be used for objective comparison of striated tool marks, and in our examples, we specifically use it for bullet signatures comparisons. The CMPS score is expected to be large if two signatures are similar. So it can also be considered as a feature that measures the similarity of two bullet signatures.

Searches for, accesses, and retrieves Statistics Canada data tables, as well as individual vectors, as tidy data frames. This package enriches the tables with metadata, deals with encoding issues, allows for bilingual English or French language data retrieval, and bundles convenience functions to make it easier to work with retrieved table data. For more efficient data access the package allows for caching data in a local database and database level filtering, data manipulation and summarizing.

Joint distribution of number of crossings and the longest run in a series of independent Bernoulli trials. The computations uses an iterative procedure where computations are based on results from shorter series. The procedure conditions on the start value and partitions by further conditioning on the position of the first crossing (or none).

This package provides a multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies. The ColocBoost model is described in Cao et. al. (2025) <doi:10.1101/2025.04.17.25326042>.

Automated and robust framework for analyzing R-R interval (RRi) signals using advanced nonlinear modeling and preprocessing techniques. The package implements a dual-logistic model to capture the rapid drop and subsequent recovery of RRi during exercise, as described by Castillo-Aguilar et al. (2025) <doi:10.1038/s41598-025-93654-6>. In addition, CardioCurveR includes tools for filtering RRi signals using zero-phase Butterworth low-pass filtering and for cleaning ectopic beats via adaptive outlier replacement using local regression and robust statistics. These integrated methods preserve the dynamic features of RRi signals and facilitate accurate cardiovascular monitoring and clinical research.

Model soil gas fluxes with the Flux-Gradient Method. It includes functions for data handling, a forward and an inverse model for flux modeling and methods for calibration and uncertainty estimation. For more details see Gartiser et al. (2025a) <doi:10.21105/joss.08094> and Gartiser et al. (2025b) <doi:10.1111/ejss.70126>.

Computes the center of gravity (COG) of character-like binary images using three different methods. This package provides functions for estimating stroke-based, contour-based, and potential energy-based COG. It is useful for analyzing glyph structure in areas such as visual cognition research and font development. The contour-based method was originally proposed by Kotani et al. (2004) <https://ipsj.ixsq.nii.ac.jp/records/36793> and Kotani (2011) <https://shonan-it.repo.nii.ac.jp/records/2000243>, while the potential energy-based method was introduced by Kotani et al. (2006) <doi:10.11371/iieej.35.296>.

This package contains the R functions needed to perform Cluster-Of-Clusters Analysis (COCA) and Consensus Clustering (CC). For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.

Evaluation of the Carlson elliptic integrals and the incomplete elliptic integrals with complex arguments. The implementations use Carlson's algorithms <doi:10.1007/BF02198293>. Applications of elliptic integrals include probability distributions, geometry, physics, mechanics, electrodynamics, statistical mechanics, astronomy, geodesy, geodesics on conics, and magnetic field calculations.

Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media.

Allow to run Cppcheck (<https://cppcheck.sourceforge.io/>) on C and C++ files with a R command or a RStudio addin. The report appears in the RStudio viewer pane as a formatted HTML file. It is also possible to get this report with a shiny application. Cppcheck can spot many error types and it can also give some recommendations on the code.

Get insight into a forest of classification trees, by calculating similarities between the trees, and subsequently clustering them. Each cluster is represented by it's most central cluster member. The package implements the methodology described in Sies & Van Mechelen (2020) <doi:10.1007/s00357-019-09350-4>.

Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).).

Facilitates the creation of xpose data objects from Nonlinear Mixed Effects (NLME) model outputs produced by Certara.RsNLME or Phoenix NLME. This integration enables users to utilize all ggplot2'-based plotting functions available in xpose for thorough model diagnostics and data visualization. Additionally, the package introduces specialized plotting functions tailored for covariate model evaluation, extending the analytical capabilities beyond those offered by xpose alone.

This package provides a daily summary of COVID-19 cases, deaths, recovered, tests, vaccinations, and hospitalizations for 230+ countries, 760+ regions, and 12000+ administrative divisions of lower level. Includes policy measures, mobility data, and geospatial identifiers. Data source: COVID-19 Data Hub <https://covid19datahub.io>.

This package implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (Journal of the American Statistical Association 2025, <doi:10.1080/01621459.2024.2422124>).

Solves control systems problems relating to time/frequency response, LTI systems design and analysis, transfer function manipulations, and system conversion.

Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error. Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.

Finds single- and two-arm designs using stochastic curtailment, as described by Law et al. (2022) <doi:10.1080/10543406.2021.2009498> and Law et al. (2021) <doi:10.1002/pst.2067> respectively. Designs can be single-stage or multi-stage. Non-stochastic curtailment is possible as a special case. Desired error-rates, maximum sample size and lower and upper anticipated response rates are inputted and suitable designs are returned with operating characteristics. Stopping boundaries and visualisations are also available. The package can find designs using other approaches, for example designs by Simon (1989) <doi:10.1016/0197-2456(89)90015-9> and Mander and Thompson (2010) <doi:10.1016/j.cct.2010.07.008>. Other features: compare and visualise designs using a weighted sum of expected sample sizes under the null and alternative hypotheses and maximum sample size; visualise any binary outcome design.

Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015> (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data.

This package provides a framework for estimating causal effects of a continuous exposure using observational data, and implementing matching and weighting on the generalized propensity score. Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29.