Accelerate the process from clinical data to medical publication, including clinical data cleaning, significant result screening, and the generation of publish-ready tables and figures.

This package provides a standard test is observed on all specimens. We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens. Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control. We treat the total sample as stratified two-phase sampling and use inverse probability weighting. We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)). See: Katki HA, Li Y, Edelstein DW, Castle PE. Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.

Calculates the carbon footprint of dairy farms based on methodologies of the International Dairy Federation and the Intergovernmental Panel on Climate Change. Includes tools for single-farm and batch analysis, report generation, and visualization. Methods follow International Dairy Federation (2022) "The IDF global Carbon Footprint standard for the dairy sector" (Bulletin of the IDF n° 520/2022) <doi:10.56169/FKRK7166> and IPCC (2019) "2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Chapter 10: Emissions from Livestock and Manure Management" <https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch10_Livestock.pdf> guidelines.

This software tool is designed to generate new cohorts utilizing data from previously instantiated cohorts. It employs interval algebra operators such as UNION, INTERSECT, and MINUS to manipulate the data within the instantiated cohorts and create new cohorts.

An R interface to Cheetah Grid', a high-performance JavaScript table widget. cheetahR allows users to render millions of rows in just a few milliseconds, making it an excellent alternative to other R table widgets. The package wraps the Cheetah Grid JavaScript functions and makes them readily available for R users. The underlying grid implementation is based on Cheetah Grid <https://github.com/future-architect/cheetah-grid>.

The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers.

Proposes Seq2seq Time-Feature Analysis using an Encoder-Decoder to project into latent space and a Forward Network to predict the next sequence.

One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci). clusterhap groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, clusterhap groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, clusterhap calculates such probability from relative frequencies.

This package provides tools for storing and managing competition results. Competition is understood as a set of games in which players gain some abstract scores. There are two ways for storing results: in long (one row per game-player) and wide (one row per game with fixed amount of players) formats. This package provides functions for creation and conversion between them. Also there are functions for computing their summary and Head-to-Head values for players. They leverage grammar of data manipulation from dplyr'.

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

Generates the scripts required to create an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database and associated documentation for supported database platforms. Leverages the SqlRender package to convert the Data Definition Language (DDL) script written in parameterized Structured Query Language (SQL) to the other supported dialects.

Facilitates the identification of counterfactual queries in structural causal models via the ID* and IDC* algorithms by Shpitser, I. and Pearl, J. (2007, 2008) <doi:10.48550/arXiv.1206.5294>, <https://jmlr.org/papers/v9/shpitser08a.html>. Provides a simple interface for defining causal diagrams and counterfactual conjunctions. Construction of parallel worlds graphs and counterfactual graphs is carried out automatically based on the counterfactual query and the causal diagram. See Tikka, S. (2023) <doi:10.32614/RJ-2023-053> for a tutorial of the package.

Use the high-precision arithmetic provided by the R package Rmpfr to compute a custom-made Gauss quadrature nodes and weights, with up to 33 nodes, using a moment-based method via moment determinants. Paul Kabaila (2022) <arXiv:2211.04729>.

This package provides functions to analyze coarse data. Specifically, it contains functions to (1) fit parametric accelerated failure time models to interval-censored survival time data, and (2) estimate the case-fatality ratio in scenarios with under-reporting. This package's development was motivated by applications to infectious disease: in particular, problems with estimating the incubation period and the case fatality ratio of a given disease. Sample data files are included in the package. See Reich et al. (2009) <doi:10.1002/sim.3659>, Reich et al. (2012) <doi:10.1111/j.1541-0420.2011.01709.x>, and Lessler et al. (2009) <doi:10.1016/S1473-3099(09)70069-6>.

This package contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.

Design functions for DCMs and other types of choice studies (including MaxDiff and other tradeoffs).

Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.

Statistical tests for the comparison between two or more alpha coefficients based on either dependent or independent groups of individuals. A web interface is available at http://comparingcronbachalphas.org. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from https:// rkward.kde.org to use this feature. The respective R package rkward cannot be installed directly from a repository, as it is a part of RKWard.

This package provides tools to process CBASS-derived PAM data efficiently. Minimal requirements are PAM-based photosynthetic efficiency data (or data from any other continuous variable that changes with temperature, e.g. relative bleaching scores) from 4 coral samples (nubbins) subjected to 4 temperature profiles of at least 2 colonies from 1 coral species from 1 site. Please refer to the following CBASS (Coral Bleaching Automated Stress System) papers for in-depth information regarding CBASS acute thermal stress assays, experimental design considerations, and ED5/ED50/ED95 thermal parameters: Nicolas R. Evensen et al. (2023) <doi:10.1002/lom3.10555> Christian R. Voolstra et al. (2020) <doi:10.1111/gcb.15148> Christian R. Voolstra et al. (2025) <doi:10.1146/annurev-marine-032223-024511>.

This package provides a flexible and robust joint test of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures, Cauchy weighted joint test (CWOT) and adaptively weighted joint test (AWOT), are proposed to accurately calculate the joint test p-value. The proposed methods are evaluated through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in pharmacogenetics (PGx) association studies. For reference, see Hong Zhang, Devan Mehrotra and Judong Shen (2022) <doi:10.13140/RG.2.2.28323.53280>.

This package provides a method for pattern discovery in weighted graphs as outlined in Thistlethwaite et al. (2021) <doi:10.1371/journal.pcbi.1008550>. Two use cases are achieved: 1) Given a weighted graph and a subset of its nodes, do the nodes show significant connectedness? 2) Given a weighted graph and two subsets of its nodes, are the subsets close neighbors or distant?

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/>.

Enables educational researchers and practitioners to calculate the curricular complexity of a plan of study, visualize its prerequisite structure at scale, and conduct customizable analyses. The original tool can be found at <https://curricularanalytics.org>. Additional functions to explore curriculum complexity from the literature are also included.

This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation).