Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.

This package provides functions for cobin and micobin regression models, a new family of generalized linear models for continuous proportional data (Y in the closed unit interval [0, 1]). It also includes an exact, efficient sampler for the Kolmogorov-Gamma random variable. For details, see Lee et al. (2025+) <doi:10.48550/arXiv.2504.15269>.

This package provides functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978â 0â 387â 72578â 9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.

Calculate the distance between single-arm observational studies using covariate information to remove heterogeneity in Network Meta-Analysis (NMA) of randomized clinical trials. Facilitate the inclusion of observational data in NMA, enhancing the comprehensiveness and robustness of comparative effectiveness research. Schmitz (2018) <doi:10.1186/s12874-018-0509-7>.

Connect to the California Data Exchange Center (CDEC) Web Service <http://cdec.water.ca.gov/>. CDEC provides a centralized database to store, process, and exchange real-time hydrologic information gathered by various cooperators throughout California. The CDEC Web Service <http://cdec.water.ca.gov/dynamicapp/wsSensorData> provides a data download service for accessing historical records.

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

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The crew.aws.batch package extends the mirai'-powered crew package with a worker launcher plugin for AWS Batch. Inspiration also comes from packages mirai by Gao (2023) <https://github.com/r-lib/mirai>, future by Bengtsson (2021) <doi:10.32614/RJ-2021-048>, rrq by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>, clustermq by Schubert (2019) <doi:10.1093/bioinformatics/btz284>), and batchtools by Lang, Bischl, and Surmann (2017). <doi:10.21105/joss.00135>.

This package provides functionality for computing support intervals for univariate parameters based on confidence intervals or parameter estimates with standard errors (Pawel et al., 2022) <doi:10.48550/arXiv.2206.12290>.

This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method.

Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.

Parameter estimation, one-step ahead forecast and new location prediction methods for spatio-temporal data.

This package contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. CASMI is a fundamental concept of a series of methods. For more information about CASMI and CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author for the latest updates.

Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).

This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with Dexcom', iPro 2', Diasend', Libre', or Carelink data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit <https://github.com/childhealthbiostatscore/R-Packages/> to download the new-user guide.

Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.

Balance sheet and income statement metrics, investment analysis methods, valuation methods, loan amortization schedules, and Capital Asset Pricing Model.

Explore and normalize American campaign finance data. Created by the Investigative Reporting Workshop to facilitate work on The Accountability Project, an effort to collect public data into a central, standard database that is more easily searched: <https://publicaccountability.org/>.

This package provides a unified interface for simplifying cloud storage interactions, including uploading, downloading, reading, and writing files, with functions for both Google Drive (<https://www.google.com/drive/>) and Amazon S3 (<https://aws.amazon.com/s3/>).

Convert MacArthur-Bates Communicative Development Inventory Words and Gestures scores to would-be scores on Words and Sentences, based on modeling from the Stanford Wordbank <https://wordbank.stanford.edu/>. See Day et al. (2025) <doi:10.1111/desc.70036>.

Compares two dataframes which have the same column structure to show the rows that have changed. Also gives a git style diff format to quickly see what has changed in addition to summary statistics.

This package provides tools for sampling from a conditional copula density decomposed via Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a sampling are those which sample as first the conditioning variables (when following the sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine (based on information criteria) among those which allow for such a conditional sampling. The package includes a function to compare scatterplot matrices and pair-dependencies of two multivariate datasets.

R functions for criterion profile analysis, Davison and Davenport (2002) <doi:10.1037/1082-989X.7.4.468> and meta-analytic criterion profile analysis, Wiernik, Wilmot, Davison, and Ones (2020) <doi:10.1037/met0000305>. Sensitivity analyses to aid in interpreting criterion profile analysis results are also included.

It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) <doi:10.1016/j.spasta.2017.12.001>.

This package performs multiple comparison procedures on curve observations among different treatment groups. The methods are applicable in a variety of situations (such as independent groups with equal or unequal sample sizes, or repeated measures) by using parametric bootstrap. References to these procedures can be found at Konietschke, Gel, and Brunner (2014) <doi:10.1090/conm/622/12431> and Westfall (2011) <doi:10.1080/10543406.2011.607751>.