This package provides functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.

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.

Simple and seamless access to a variety of StatCan shapefiles for mapping Canadian provinces, regions, forward sortation areas, census divisions, and subdivisions using the popular ggplot2 package.

An implementation of several functions for feature extraction in categorical time series datasets. Specifically, some features related to marginal distributions and serial dependence patterns can be computed. These features can be used to feed clustering and classification algorithms for categorical time series, among others. The package also includes some interesting datasets containing biological sequences. Practitioners from a broad variety of fields could benefit from the general framework provided by ctsfeatures'.

Fits semiparametric joint models for longitudinal electronic health record (EHR) data that addresses two-stage hierarchical missingness mechanism. The first stage is the visiting process, and the second stage is the observation process. The core CIMEHR method (Clinical Informative Missingness for Electronic Health Records) uses a three-stage procedure: partial likelihood with log-normal frailty for visit intensity, probit regression with shared latent factor-linked random effects for observation, and weighted least squares with risk-set centering for the outcome. These three stages are connected through a shared latent factor that induces dependence across all three processes. A data simulator and implementations of common benchmark methods (linear mixed models, multiple imputation, and others) are included for comparative studies. Detailed methods are described in Yang, Shi, and Mukherjee (2026) <doi:10.48550/arXiv.2602.15374>.

This package provides tools for the combination of individual study results in meta-analyses using p-value functions. Implements various combination methods including those by Fisher, Stouffer, Tippett, Edgington along with weighted generalizations. Contains functionality for the visualization and calculation of confidence curves and drapery plots to summarize evidence across studies.

This package provides a comprehensive collection of statistical functions for climate change research. Provides tools for temporal trend detection based on the Mann-Kendall (MK) test (Mann 1945 <doi:10.2307/1907187>; Kendall 1975, ISBN:0852641990) and Sen's slope (Sen 1968 <doi:10.2307/2285891>), spatial autocorrelation using Moran's I (Moran 1950 <doi:10.2307/2332142>), extreme value analysis using the Generalised Extreme Value (GEV) distribution and Peaks-Over-Threshold (POT) method (Coles 2001 <doi:10.1007/978-1-4471-3675-0>), standardised drought indices including the Standardised Precipitation Index (SPI; McKee et al. 1993) and the Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al. 2010 <doi:10.1175/2009JCLI2909.1>), and formal detection-attribution methods via optimal fingerprint regression and Empirical Orthogonal Function (EOF) analysis (Allen and Tett 1999 <doi:10.1007/s003820050291>), and apparent temperature via the heat index (Steadman 1979 <doi:10.1175/1520-0450(1979)018%3C0861:TAOSPI%3E2.0.CO;2>). Suitable for both station-level time series and gridded climate fields.

Estimate sample sizes needed to capture target levels of genetic diversity from a population (multivariate allele frequencies) for applications like germplasm conservation and breeding efforts. Compares bootstrap samples to a full population using linear regression, employing the R-squared value to represent the proportion of diversity captured. Iteratively increases sample size until a user-defined target R-squared is met. Offers a parallelized R implementation of a previously developed python method. All ploidy levels are supported. For more details, see Sandercock et al. (2024) <doi:10.1073/pnas.2403505121>.

This package provides functions for calculating clinical significance.

Chromosome files in the Fasta format usually contain large sequences like human genome. Sometimes users have to split these chromosomes into different files according to their chromosome number. The chromseq can help to handle this. So the selected chromosome sequence can be used for downstream analysis like motif finding. Howard Y. Chang(2019) <doi:10.1038/s41587-019-0206-z>.

This package provides authentication for Shiny applications using Amazon Cognito ( <https://aws.amazon.com/es/cognito/>).

An API wrapper for Cryptowatch to get prices and other information (e.g., volume, trades, order books, bid and ask prices, live quotes, and more) about cryptocurrencies and crypto exchanges. See <https://docs.cryptowat.ch/rest-api> for a detailed documentation.

This package provides a set of tools that can be used across data.frame and imputationList objects.

Determining the value of Stirling numbers of 1st kind and 2nd kind,references: Bóna,Miklós(2017,ISBN 9789813148840).

The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.

This package performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in Greifer, et al. (2025) <doi:10.32614/RJ-2024-015>. clarify is meant to replace some of the functionality of the archived package Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.

Estimate one or two cutpoints of a metric or ordinal-scaled variable in the multivariable context of survival data or time-to-event data. Visualise the cutpoint estimation process using contour plots, index plots, and spline plots. It is also possible to estimate cutpoints based on the assumption of a U-shaped or inverted U-shaped relationship between the predictor and the hazard ratio. Govindarajulu, U., and Tarpey, T. (2022) <doi:10.1080/02664763.2020.1846690>.

There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the article titled New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs published in Scientific Reports. Please see the reference: Yu et al. (2019) <doi:10.1038/s41598-019-40167-8>.

This package provides functions for testing if the covariance structure of 2-dimensional data (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2. A complete descriptions of the implemented tests can be found in the paper Aston et al. (2017) <doi:10.1214/16-AOS1495> <doi:10.48550/arXiv.1505.02023>.

Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame.

This package provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus <https://systems.jhu.edu/research/public-health/ncov/>.

This package provides functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of clusteredness in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2018) <doi:10.1080/10618600.2017.1349664>. We provide an R native version with methods for printing, summarizing, and plotting the result.

Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) <doi:10.1126/science.1132939>. It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot.

Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.