Helpful functions for the cleaning and manipulation of surveillance data, especially with regards to the creation and validation of panel data from individual level surveillance data.

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.

This package provides R users with direct access to genomic and clinical data from the cBioPortal web resource via user-friendly functions that wrap cBioPortal's existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations, copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'), microsatellite instability status ('MSI'), FACETS and select clinical data points (depending on the study). See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.

Uses data from the EPSG Registry to look up suitable coordinate reference system transformations for spatial datasets in R. Returns a data frame with CRS codes that can be used for CRS transformation and mapping projects. Please see the EPSG Dataset Terms of Use at <https://epsg.org/terms-of-use.html> for more information.

This package contains functions for solving commonly encountered problems while programming in R. This package is intended to provide a lightweight supplement to Base R, and will be useful for almost any R user.

This package provides functions for constructing and evaluating CUSUM charts and RA-CUSUM charts with focus on false signal probability.

Systematically Run R checks against multiple packages. Checks are run in parallel with strategies to minimize dependency installation. Provides out of the box interface for running reverse dependency check.

Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

Allows you to conduct robust correlations on your non-normal data set. The robust correlations included in the package are median-absolute-deviation and median-based correlations. Li, J.C.H. (2022) <doi:10.5964/meth.8467>.

This package provides conversion functionality between a broad range of scientific, historical, and industrial unit types.

Measuring cellular energetics is essential to understanding a matrixâ s (e.g. cell, tissue or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization using the methods described by Mookerjee et al. (2017) <doi:10.1074/jbc.m116.774471>.

The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.

Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or Bartlett scores (including bias correction using Croonâ s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.).

Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://www.cs.princeton.edu/courses/archive/spr06/cos423/Handouts/EPP%20shortest%20path%20algorithms.pdf>), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>), new bidirectional A* (Pijls & Post (2009) <https://repub.eur.nl/pub/16100/ei2009-10.pdf>), Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) <doi:10.1007/978-3-540-68552-4_24>), PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) <doi:10.1016/j.jpdc.2012.02.007>). Algorithms for solving the traffic assignment problem are All-or-Nothing assignment, Method of Successive Averages, Frank-Wolfe algorithm (M. Fukushima (1984) <doi:10.1016/0191-2615(84)90029-8>), Conjugate and Bi-Conjugate Frank-Wolfe algorithms (M. Mitradjieva, P. O. Lindberg (2012) <doi:10.1287/trsc.1120.0409>), Algorithm-B (R. B. Dial (2006) <doi:10.1016/j.trb.2006.02.008>).

Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) <doi:10.48550/arXiv.2410.13113>.

This package provides a tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) <doi:10.48550/arXiv.2505.20168>. Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the metafor package <https://CRAN.R-project.org/package=metafor>.

Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. The package makes it easy to incorporate constraints into the model's coefficients. The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), a vector of bound constraints for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions among coefficients (i.e beta1 > beta2). The references of this packages are the same as stats package for glm() and arima() functions. See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528). For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages.

Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>.

This package provides a Bayesian approach to using predictive probability in an ANOVA construct with a continuous normal response, when threshold values must be obtained for the question of interest to be evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). The Bayesian Mission Mean (BMM) is used to evaluate a question of interest (that is, a mean that randomly selects combination of factor levels based on their probability of occurring instead of averaging over the factor levels, as in the grand mean). Under this construct, in contrast to a Gibbs sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is required. The nested sampler determines the conditional posterior distribution of the model parameters, given Y, and the outside sampler determines the marginal posterior distribution of Y (also commonly called the predictive distribution for Y). This approach provides a sample from the joint posterior distribution of Y and the model parameters, while also accounting for the threshold value that must be obtained in order for the question of interest to be evaluated as successful.

This package implements the chain binomial model for analysis of infectious disease data. Contains functions for calculating probabilities of the final size of infectious disease outbreaks using the method from D. Ludwig (1975) <doi:10.1016/0025-5564(75)90119-4> and for outbreaks that are not concluded, from Lindstrøm et al. (2024) <doi:10.48550/arXiv.2403.03948>. The package also contains methods for estimation and regression analysis of secondary attack rates.

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

This package provides methods and plotting functions for displaying categorical data on an interactive heatmap using plotly'. Provides functionality for strictly categorical heatmaps, heatmaps illustrating categorized continuous data and annotated heatmaps. Also, there are various options to interact with the x-axis to prevent overlapping axis labels, e.g. via simple sliders or range sliders. Besides the viewer pane, resulting plots can be saved as a standalone HTML file, embedded in R Markdown documents or in a Shiny app.

As different antipsychotic medications have different potencies, the doses of different medications cannot be directly compared. Various strategies are used to convert doses into a common reference so that comparison is meaningful. Chlorpromazine (CPZ) has historically been used as a reference medication into which other antipsychotic doses can be converted, as "chlorpromazine-equivalent doses". Using conversion keys generated from widely-cited scientific papers, e.g. Gardner et. al 2010 <doi:10.1176/appi.ajp.2009.09060802> and Leucht et al. 2016 <doi:10.1093/schbul/sbv167>, antipsychotic doses are converted to CPZ (or any specified antipsychotic) equivalents. The use of the package is described in the included vignette. Not for clinical use.

This package provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal <https://portaldatransparencia.gov.br/download-de-dados/cpgf/> and a curated dataset hosted on the Open Science Framework <https://osf.io/z2mxc/>. Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism.