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.

This package provides a basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.

Automated method for doublet detection in flow or mass cytometry data, based on simulating doublets and finding events whose protein expression patterns are similar to the simulated doublets.

Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. Estimation of copula parameter(COPC) and Conditional quantile estimation are included for five available copula functions. Copula selection methods based on L2 distance from empirical copula function are also included.

Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).

This package contains the probability density function, cumulative distribution function, quantile function, and random number generator for composite and discrete composite distributions with Pareto tails. The detailed description of the methods and the applications of the methods can be found in Bowen Liu, Malwane M.A. Ananda (2023) <arXiv:2309.16443>.

Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package.

One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.

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'.

Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency.

We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.

Imports and cleans opencovid19-fr <https://github.com/opencovid19-fr/data> data on COVID-19 in France.

This package provides a wrapper for the Clockify API <https://docs.clockify.me/>, making it possible to query, insert and update time keeping data.

Providing data to quickly visualize and analyze data from several cryptocurrencies.

This package provides a shiny app to discover cocktails. The app allows one to search for cocktails by ingredient, filter on rating, and number of ingredients. The package also contains data with the ingredients of nearly 26 thousand cocktails scraped from the web.

Fits a pseudo Cox proprotional hazards model when survival times are missing for control groups.

This package provides tools for interacting with the Circle CI API (<https://circleci.com/docs/api/v2/>). Besides executing common tasks such as querying build logs and restarting builds, this package also helps setting up permissions to deploy from builds.

Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and McCaffrey <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ>. Functions used in Huang and Li <doi:10.3758/s13428-021-01627-0>, Huang, Wiedermann', and Zhang <doi:10.1080/00273171.2022.2077290>, and Huang, Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness).

Enhances the ini package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where %(....)s instances are interpolated by other included options or outside variables.

This package provides tools to interface with Cytobank's API via R, organized by endpoints that represent various areas of Cytobank functionality. Learn more about Cytobank at <https://www.beckman.com/flow-cytometry/software>.

Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, <https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/2020/2016.pdf>.

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

This package provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) <doi:10.1111/biom.13716>, Robertson et al. (2021) <doi:10.48550/arXiv.2104.05905>, and Wang et al. (2024) <doi:10.48550/arXiv.2402.02684>. The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2025) <doi:10.1017/rsm.2025.5> for a detailed guide on using the package.

Engines for survival models from the parsnip package. These include parametric models (e.g., Jackson (2016) <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g., Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).