Download imagery tiles to a standard cache and load the data into raster objects. Facilities for AWS terrain <https://registry.opendata.aws/terrain-tiles/> terrain and Mapbox <https://www.mapbox.com/> servers are provided.

Inference with control function methods for nonlinear outcome models when the model is known ('Guo and Small (2016) <arXiv:1602.01051>) and when unknown but semiparametric ('Li and Guo (2021) <arXiv:2010.09922>).

It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.

Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.

Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner.

Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>. For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics <DOI:10.1007/s10614-021-10218-2>.

This R-extension package contains dozens of functions useful for: database style joins [nerge()] & aggregation [bestBy(), groupBy() & regroup()], database migration [dbWriteTable2()], file I/O [write.delim(), read.tab()], text parsing / data mining [m()], data structure conversion [nv(), tab2df()], summarizing & reporting [pct(), fit.1ln.rprt()], character string manipulation [m() & pad()], legend table making [sstable() & leghead()] & plot placement [legend.position()], plot annotation [labsegs() & mvlabs()], data visualization [pies(), spie(), & heatmatrix()], and data exploration [hyperplot(), plot.xy.ab.p()], batch scripting [parseArgString()]. The package's greatest contributions stem from its database style merge, aggregation and interface functions as well as in it's extensive use and propagation of row, column and vector names in most functions. The latest additions are plotting functions [confound.grid() & sparge()] that intake a dataframe & formulas to visually resolve variable confounding (Simpson, 1951).

Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.

Computes a single scalar metric for diurnal cortisol cycle analysis, the Cortisol Sine Score (CSS). The score is calculated as the sum over time points of concentration multiplied by sin(2 * pi * time / 24), giving positive weights to morning time points and negative weights to evening ones. The method is model-free, robust, and suitable for regression, classification, clustering, and biomarker research.

Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. CompMix package allows practitioners to estimate the health impacts from exposure to chemical mixtures data through various statistical approaches, including Lasso, Elastic net, Bayesian kernel machine regression (BKMR), hierNet, Quantile g-computation, Weighted quantile sum (WQS) and Random forest. Methods and recommendations are described in Hao et al. (2025) <doi:10.1289/EHP15305>.

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.

Several nonparametric estimators of autocovariance functions. Procedures for constructing their confidence regions by using bootstrap techniques. Methods to correct autocovariance estimators and several tools for analysing and comparing them. Supplementary functions, including kernel computations and discrete cosine Fourier transforms. For more details see Bilchouris and Olenko (2025) <doi:10.17713/ajs.v54i1.1975>.

Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2022) <doi:10.1080/10618600.2021.2000873>. Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this. There are options for different types of graphics to assess the influence.

This package provides a compilation of tests for hypotheses regarding covariance and correlation matrices for one or more groups. The hypothesis can be specified through a corresponding hypothesis matrix and a vector or by choosing one of the basic hypotheses, while for the structure test, only the latter works. Thereby Monte-Carlo and Bootstrap-techniques are used, and the respective method must be chosen, and the functions provide p-values and mostly also estimators of calculated covariance matrices of test statistics. For more details on the methodology, see Sattler et al. (2022) <doi:10.1016/j.jspi.2021.12.001>, Sattler and Pauly (2024) <doi:10.1007/s11749-023-00906-6>, Sattler and Dobler (2026) <doi:10.1016/j.jmva.2025.105517>, and Sattler and Jedhoff (2025) <doi:10.48550/arXiv.2507.03406>.

Customized training is a simple technique for transductive learning, when the test covariates are known at the time of training. The method identifies a subset of the training set to serve as the training set for each of a few identified subsets in the training set. This package implements customized training for the glmnet() and cv.glmnet() functions.

We propose a method to estimate the probability of an undetected case of COVID-19 in a defined setting, when a given number of people have been exposed, with a given pretest probability of having COVID-19 as a result of that exposure. Since we are interested in undetected COVID-19, we assume no person has developed symptoms (which would warrant further investigation) and that everyone was tested on a given day, and all tested negative.

Common API for filtering data stored in different data models. Provides multiple filter types and reproducible R code. Works standalone or with shinyCohortBuilder as the GUI for interactive Shiny apps.

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.

Auto, Cross and Multi-dimensional recurrence quantification analysis. Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) <doi:10.32614/RJ-2021-062>, Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510> and Wallot (2018) <doi: 10.1080/00273171.2018.1512846> for further details about the method.

Uses inverse probability weighting methods to estimate treatment effect under marginal structure model for the cause-specific hazard of competing risk events. Estimates also the cumulative incidence function (i.e. risk) of the potential outcomes, and provides inference on risk difference and risk ratio. Reference: Kalbfleisch & Prentice (2002)<doi:10.1002/9781118032985>; Hernan et al (2001)<doi:10.1198/016214501753168154>.

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>.

This package provides functions for estimating and reporting multi-year averages and corresponding confidence intervals and distributions. A potential use case is reporting the chemical and ecological status of surface waters according to the European Water Framework Directive.

Tables summarizing clinical trial results are often complex and require detailed tailoring prior to submission to a health authority. The crane package supplements the functionality of the gtsummary package for creating these often highly bespoke tables in the pharmaceutical industry.

This package creates auto-grading check-fields and check-boxes for rmarkdown or quarto HTML. It can be used in class, when teacher share materials and tasks, so students can solve some problems and check their work. In contrast to the learnr package, the checkdown package works serverlessly without shiny'.