Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) <doi:10.48550/arXiv.2206.02296>; Rava (2021) <https://escholarship.org/uc/item/8h1846gs>.

This package provides a dashboard supports the usage of cromwell'. Cromwell is a scientific workflow engine for command line users. This package utilizes cromwell REST APIs and provides these convenient functions: timing diagrams for running workflows, cromwell engine status, a tabular workflow list. For more information about cromwell', visit <http://cromwell.readthedocs.io>.

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>.

Fetches the Cornell Lab of Ornithology Open Tree of Life (clootl) tree in a specified taxonomy. Optionally prune it to a given set of study taxa. Provide a recommended citation list for the studies that informed the extracted tree. Tree generated as described in McTavish et al. (2024) <doi:10.1101/2024.05.20.595017>.

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on future (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>) and mirai (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends, terra (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and sf (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in terra or sf packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses exactextractr (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided.

This package provides a graphical user interface for simulating the effects of mergers, tariffs, and quotas under an assortment of different economic models. The interface is powered by the Shiny web application framework from RStudio'.

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>.

Analyze and compare conversations using various similarity measures including topic, lexical, semantic, structural, stylistic, sentiment, participant, and timing similarities. Supports both pairwise conversation comparisons and analysis of multiple dyads. Methods are based on established research: Topic modeling: Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>; Landauer et al. (1998) <doi:10.1080/01638539809545028>; Lexical similarity: Jaccard (1912) <doi:10.1111/j.1469-8137.1912.tb05611.x>; Semantic similarity: Salton & Buckley (1988) <doi:10.1016/0306-4573(88)90021-0>; Mikolov et al. (2013) <doi:10.48550/arXiv.1301.3781>; Pennington et al. (2014) <doi:10.3115/v1/D14-1162>; Structural and stylistic analysis: Graesser et al. (2004) <doi:10.1075/target.21131.ryu>; Sentiment analysis: Rinker (2019) <https://github.com/trinker/sentimentr>.

This package provides a lightweight data validation and testing toolkit for R. Its guiding philosophy is that adding code-based data checks to users existing workflow should be both quick and intuitive. The suite of functions included therefore mirror the common data checks many users already perform by hand or by eye. Additionally, the checkthat package is optimized to work within tidyverse data manipulation pipelines.

This package implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements optimal CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>, several extensions of the CBPS beyond the cross-sectional, binary treatment setting. They include the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) <DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates.

Evaluation for density and distribution function of convolution of gamma distributions in R. Two related exact methods and one approximate method are implemented with efficient algorithm and C++ code. A quick guide for choosing correct method and usage of this package is given in package vignette. For the detail of methods used in this package, we refer the user to Mathai(1982)<doi:10.1007/BF02481056>, Moschopoulos(1984)<doi:10.1007/BF02481123>, Barnabani(2017)<doi:10.1080/03610918.2014.963612>, Hu et al.(2020)<doi:10.1007/s00180-019-00924-9>.

This package provides methods and data for color science - color conversions by observer, illuminant, and gamma. Color matching functions and chromaticity diagrams. Color indices, color differences, and spectral data conversion/analysis. This package is deprecated and will someday be removed; for reasons and details please see the README file.

This package provides a set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.

In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, <DOI:10.1002/gepi.22107>) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison.

Random sampling from distributions with user-specified population covariance matrix. Marginal information may be fully specified, for which the package implements the VITA (VIne-To-Anything) algorithm Grønneberg and Foldnes (2017) <doi:10.1007/s11336-017-9569-6>. See also Grønneberg, Foldnes and Marcoulides (2022) <doi:10.18637/jss.v102.i03>. Alternatively, marginal skewness and kurtosis may be specified, for which the package implements the IG (independent generator) and PLSIM (piecewise linear) algorithms, see Foldnes and Olsson (2016) <doi:10.1080/00273171.2015.1133274> and Foldnes and Grønneberg (2021) <doi:10.1080/10705511.2021.1949323>, respectively.

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>.

The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) <arXiv:1511.04992>. If the rho= argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) <DOI:10.1214/07-AOS574>. The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen.

An educational package providing intuitive functions for calculating confidence intervals (CI) for various statistical parameters. Designed primarily for teaching and learning about statistical inference (particularly confidence intervals). Offers user-friendly wrappers around established methods for proportions, means, and bootstrap-based intervals. Integrates seamlessly with Tidyverse workflows, making it ideal for classroom demonstrations and student exercises.

Calculate the theoretical value of convertible bonds by given parameters, including B-S theory and Monte Carlo method.

Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). <doi:10.1177/09622802241236933>).

Copula-based imputation methods: parametric and nonparametric algorithms for missing multivariate data through conditional copulas.

The level-dependent cross-validation method is implemented for the selection of thresholding value in wavelet shrinkage. This procedure is implemented by coupling a conventional cross validation with an imputation method due to a limitation of data length, a power of 2. It can be easily applied to classical leave-one-out and k-fold cross validation. Since the procedure is computationally fast, a level-dependent cross validation can be performed for wavelet shrinkage of various data such as a data with correlated errors.

One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.

Balancing and rounding matrices subject to restrictions. Adjustment of matrices so that columns and rows add up to given vectors, rounding of a matrix while keeping the column and/or row totals, performing these by blocks...