In data science, it is a common practice to compute a series of columns (e.g. features) against a common response vector. Various metrics are provided with efficient computation implemented with Rcpp'.

Allows the user to generate and execute select, insert, update and delete SQL queries the underlying database without having to explicitly write SQL code.

Superclasses PostgreSQL connection to help enable full dplyr functionality on Redshift'.

This package provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.

SyncroSim is a generalized framework for managing scenario-based datasets (<https://syncrosim.com/>). rsyncrosim provides an interface to SyncroSim'. Simulation models can be added to SyncroSim in order to transform these datasets, taking advantage of general features such as defining scenarios of model inputs, running Monte Carlo simulations, and summarizing model outputs. rsyncrosim requires SyncroSim 2.3.5 or higher (API documentation: <https://docs.syncrosim.com/>).

Enables researchers to conduct multivariate statistical analyses of survey data with randomized response technique items from several designs, including mirrored question, forced question, and unrelated question. This includes regression with the randomized response as the outcome and logistic regression with the randomized response item as a predictor. In addition, tools for conducting power analysis for designing randomized response items are included. The package implements methods described in Blair, Imai, and Zhou (2015) Design and Analysis of the Randomized Response Technique, Journal of the American Statistical Association <https://graemeblair.com/papers/randresp.pdf>.

This package provides a collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.

Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).

Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.

R Markdown format for reveal.js presentations, a framework for easily creating beautiful presentations using HTML.

This package provides and extends the Fuzzy Coco algorithm by wrapping the FuzzyCoCo C++ Library, cf <https://github.com/Lonza-RND-Data-Science/fuzzycoco>. Fuzzy Coco constructs systems that predict the outcome of a human decision-making process while providing an understandable explanation of a possible reasoning leading to it. The constructed fuzzy systems are composed of rules and linguistic variables. This package provides a S3 classic interface (fit_xy()/fit()/predict()/evaluate()) and a tidymodels'/'parsnip interface, a custom engine with custom iteration stop criterion and progress bar support as well as a systematic implementation that do not rely on genetic programming but rather explore all possible combinations.

R2 statistic for significance test. Variance and covariance of R2 values used to assess the 95% CI and p-value of the R2 difference.

This package implements the P-model (Stocker et al., 2020 <doi:10.5194/gmd-13-1545-2020>), predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis, assimilation, and dark respiration rates as a function of the environment (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).

This package implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow. To appear in Journal of the Royal Statistical Society: Series B. <arXiv:1604.08697>.

Perform a regression analysis, generate a regression table, create a scatter plot, and download the results. It uses stargazer for generating regression tables and ggplot2 for creating plots. With just two lines of code, you can perform a regression analysis, visualize the results, and save the output. It is part of my make R easy project where one doesn't need to know how to use various packages in order to get results and makes it easily accessible to beginners. This is a part of my make R easy project. Help from ChatGPT was taken. References were Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.

This package provides functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.

High level and easy HTTP client for R'. Provides functions for building HTTP queries, including query parameters, body requests, headers, authentication, and more.

R on FHIR is an easy to use wrapper around the HL7 FHIR REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official HL7 FHIR .NET API', also made by Firely.

Automated performance of common transformations used to fulfill parametric assumptions of normality and identification of the best performing method for the user. Output for various normality tests (Thode, 2002) corresponding to the best performing method and a descriptive statistical report of the input data in its original units (5-number summary and mathematical moments) are also presented. Lastly, the Rankit, an empirical normal quantile transformation (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard use cases and facilitate adoption. <DOI: 10.1201/9780203910894>. <DOI: 10.22237/jmasm/1257034080>.

Loads Blackrock <https://blackrockneurotech.com> neural signal data files into the memory, provides utility tools to extract the data into common formats such as plain-text tsv and HDF5'.

Supporting decision making involving multiple criteria. Annice Najafi, Shokoufeh Mirzaei (2025) RMCDA: The Comprehensive R Library for applying multi-criteria decision analysis methods, Volume 24, e100762 <doi:10.1016/j.simpa.2025.100762>.

This package provides methods to calculate approximate regional consistency probabilities using Method 1 and Method 2 proposed by the Japanese Ministry of Health, Labor and Welfare (2007) <https://www.pmda.go.jp/files/000153265.pdf>. These methods are useful for assessing regional consistency in multi-regional clinical trials. The package can calculate unconditional, joint, and conditional regional consistency probabilities. For technical details, please see Homma (2024) <doi:10.1002/pst.2358>.

Aims at loading Criteo online advertising campaign data into R. Criteo <http://www.criteo.com/> is an online advertising service that enables advertisers to display commercial ads to web users. The package provides an authentication process for R with the Criteo API <http://kb.criteo.com/ advertising/content/5/27/en/api.html>. Moreover, the package features an interface to query campaign data from the Criteo API. The data can be downloaded and will be transformed into a R data frame.