Researchers can characterize and learn about the properties of research designs before implementation using `DeclareDesign`. Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued.

Query for metrics from Datadog (<https://www.datadoghq.com/>) via its API.

Automatic differentiation is achieved by using dual numbers without providing hand-coded gradient functions. The output value of a mathematical function is returned with the values of its exact first derivative (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind (2018) <https://jmlr.org/papers/volume18/17-468/17-468.pdf>.

Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and DESeq2 (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) <doi:10.1101/2025.01.31.634335>.

This package provides methods for reading, displaying, processing and writing files originally arranged for the DSSAT-CSM fixed width format. The DSSAT-CSM cropping system model is described at J.W. Jones, G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>.

Decorrelates a set of summary statistics (i.e., Z-scores or P-values per SNP) via Decorrelation by Orthogonal Transformation (DOT) approach and performs gene-set analyses by combining transformed statistic values; operations are performed with algorithms that rely only on the association summary results and the linkage disequilibrium (LD). For more details on DOT and its power, see Olga (2020) <doi:10.1371/journal.pcbi.1007819>.

This is the core package that provides both the user API and developer API to deploy the parallel cluster on the cloud using the container service. The user can call clusterPreset() to define the cloud service provider and container and makeDockerCluster() to create the cluster. The developer should see "developer's cookbook" on how to define the cloud provider and container.

Estimation of a density from grouped (tabulated) summary statistics evaluated in each of the big bins (or classes) partitioning the support of the variable. These statistics include class frequencies and central moments of order one up to four. The log-density is modelled using a linear combination of penalised B-splines. The multinomial log-likelihood involving the frequencies adds up to a roughness penalty based on the differences in the coefficients of neighbouring B-splines and the log of a root-n approximation of the sampling density of the observed vector of central moments in each class. The so-obtained penalized log-likelihood is maximized using the EM algorithm to get an estimate of the spline parameters and, consequently, of the variable density and related quantities such as quantiles, see Lambert, P. (2021) <arXiv:2107.03883> for details.

This package implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.

Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, DIVINE makes the process more efficient, transparent, and reproducible.

Calculates Days Alive and Out of Hospital (DAOH) from administrative admission/discharge/mortality data using three algorithms (nights, days, exact) and three death-handling approaches (midday, midnight, zero). Includes tools for comparing methods (Bland-Altman, ICC, reclassification), and plotting.

This package provides a HTML widget that shows differences between files (text, images, and data frames).

This package performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) <doi:10.18637/jss.v081.i12>). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) <doi:10.18637/jss.v027.i08>). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)).

Fit and explore Drift Diffusion Models (DDMs), a common tool in psychology for describing decision processes in simple tasks. It can handle both time-independent and time-dependent DDMs. You either choose prebuilt models or create your own, and the package takes care of model predictions and parameter estimation. Model predictions are derived via the numerical solutions provided by Richter, Ulrich, and Janczyk (2023, <doi:10.1016/j.jmp.2023.102756>).

Analysis, visualisation and simulation of digital polymerase chain reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). Supports data formats of commercial systems (Bio-Rad QX100 and QX200; Fluidigm BioMark) and other systems.

Create shareable data sets from raw data files that contain protected elements. Relying on master crosswalk files that list restricted variables, package functions warn users about possible violations of data usage agreement and prevent writing protected elements.

Estimates heterogeneous coefficient models for large panels with cross-sectional dependence. Implements the Mean Group (MG) estimator of Pesaran and Smith (1995) <doi:10.1016/0304-4076(94)01644-F>, the Common Correlated Effects (CCE) and Dynamic CCE (DCCE) estimators of Pesaran (2006) <doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran (2015) <doi:10.1016/j.jeconom.2015.03.007>, the regularized CCE of Juodis (2022), the Augmented Mean Group (AMG) of Eberhardt and Teal (2010), the Interactive Fixed Effects (IFE) estimator of Bai (2009) <doi:10.3982/ECTA6135>, and long-run estimators including Cross-Sectionally augmented Distributed Lag (CS-DL), Cross-Sectionally augmented Autoregressive Distributed Lag (CS-ARDL), and Pooled Mean Group (PMG) (Chudik et al. 2016; Shin et al. 1999). Also provides rolling-window estimation, high-dimensional fixed effect absorption, spatial CCE via user-supplied weight matrices, and structural break tests (Chow and sup-Wald) following Andrews (1993), Bai and Perron (1998), and Ditzen, Karavias and Westerlund (2024). Supplies a comprehensive cross-sectional dependence (CD) test suite including the Pesaran (2015) CD test <doi:10.1080/07474938.2014.956623>, the Juodis and Reese (2022) randomized weighted CD (CDw) test, the Baltagi et al. (2012) bias-adjusted weighted CD (CDw+) test, the Fan et al. (2015) Power Enhancement Approach (PEA) test, and the Pesaran and Xie (2021) bias-corrected CD (CD*) test. Further diagnostics include the Pesaran (2007) Cross-sectionally Augmented IPS (CIPS) panel unit root test <doi:10.1002/jae.951>, the Westerlund (2007) panel cointegration tests, the Dumitrescu and Hurlin (2012) panel Granger causality test, the Im-Pesaran-Shin (IPS) and Levin-Lin-Chu (LLC) panel unit root tests, the Pedroni (2004) and Kao (1999) residual cointegration tests, the Swamy (1970) and Pesaran and Yamagata (2008) slope homogeneity tests, a Hausman-type test for MG versus pooled, the exponent of cross-sectional dependence from Bailey et al. (2016) <doi:10.1002/jae.2490>, information criteria for Cross-Sectional Average (CSA) selection, the rank condition classifier, impulse response functions, cross-section and wild bootstrap inference, and broom'-compatible methods.

This package provides a set of functions for securely storing API tokens and interacting with the <https://diariodeobras.net> system. Includes convenient wrappers around the httr2 package to perform authenticated requests, retrieve project details, tasks, reports, and more.

Creating, and refining data nuggets. Data nuggets reduce a large dataset into a small collection of nuggets of data, each containing a center (location), weight (importance), and scale (variability) parameter. Data nugget centers are created by choosing observations in the dataset which are as equally spaced apart as possible. Data nugget weights are created by counting the number observations closest to a given data nugget center. We then say the data nugget contains these observations and the data nugget center is recalculated as the mean of these observations. Data nugget scales are created by calculating the trace of the covariance matrix of the observations contained within a data nugget divided by the dimension of the dataset. Data nuggets are refined by splitting data nuggets which have scales or shapes (defined as the ratio of the two largest eigenvalues of the covariance matrix of the observations contained within the data nugget) Reference paper: [1] Beavers, T. E., Cheng, G., Duan, Y., Cabrera, J., Lubomirski, M., Amaratunga, D., & Teigler, J. E. (2024). Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure. Journal of Computational and Graphical Statistics, 1-21. [2] Cherasia, K. E., Cabrera, J., Fernholz, L. T., & Fernholz, R. (2022). Data Nuggets in Supervised Learning. \emphIn Robust and Multivariate Statistical Methods: Festschrift in Honor of David E. Tyler (pp. 429-449). Cham: Springer International Publishing.

Reaction rate dynamics can be retrieved from metabolite concentration time courses. User has to provide corresponding stoichiometric matrix but not a regulation model (Michaelis-Menten or similar). Instead of solving an ordinary differential equation (ODE) system describing the evolution of concentrations, we use B-splines to catch the concentration and rate dynamics then solve a least square problem on their coefficients with non-negativity (and optionally monotonicity) constraints. Constraints can be also set on initial values of concentration. The package dynafluxr can be used as a library but also as an application with command line interface dynafluxr::cli("-h") or graphical user interface dynafluxr::gui().

This package provides a system for the management, assessment, and psychometric analysis of data from educational and psychological tests.

R interface for the Google Cloud Services Document AI API <https://cloud.google.com/document-ai> with additional tools for output file parsing and text reconstruction. Document AI is a powerful server-based OCR service that extracts text and tables from images and PDF files with high accuracy. daiR gives R users programmatic access to this service and additional tools to handle and visualize the output. See the package website <https://dair.info/> for more information and examples.

Plan optimal sample size allocation and go/no-go decision rules for phase II/III drug development programs with time-to-event, binary or normally distributed endpoints when assuming fixed treatment effects or a prior distribution for the treatment effect, using methods from Kirchner et al. (2016) <doi:10.1002/sim.6624> and Preussler (2020). Optimal is in the sense of maximal expected utility, where the utility is a function taking into account the expected cost and benefit of the program. It is possible to extend to more complex settings with bias correction (Preussler S et al. (2020) <doi:10.1186/s12874-020-01093-w>), multiple phase III trials (Preussler et al. (2019) <doi:10.1002/bimj.201700241>), multi-arm trials (Preussler et al. (2019) <doi:10.1080/19466315.2019.1702092>), and multiple endpoints (Kieser et al. (2018) <doi:10.1002/pst.1861>).

Makes deck.gl <https://deck.gl/>, a WebGL-powered open-source JavaScript framework for visual exploratory data analysis of large datasets, available within R via the htmlwidgets package. Furthermore, it supports basemaps from mapbox <https://www.mapbox.com/> via mapbox-gl-js <https://github.com/mapbox/mapbox-gl-js>.