Given a set of predictive quantiles from a distribution, estimate the distribution and create `d`, `p`, `q`, and `r` functions to evaluate its density function, distribution function, and quantile function, and generate random samples. On the interior of the provided quantiles, an interpolation method such as a monotonic cubic spline is used; the tails are approximated by a location-scale family.

This package provides a comprehensive and dynamic configuration driven logging package for R. While there are several excellent logging solutions already in the R ecosystem, I always feel constrained in some way by each of them. Every project is designed differently to solve it's domain specific problem, and ultimately the utility of a logging solution is its ability to adapt to this design. This is the raison d'être for dyn.log': to provide a modular design, template mechanics and a configuration-based integration model, so that the logger can integrate deeply into your design, even though it knows nothing about it.

This package provides a collection of widely used univariate data sets of various applied domains on applications of distribution theory. The functions allow researchers and practitioners to quickly, easily, and efficiently access and use these data sets. The data are related to different applied domains and as follows: Bio-medical, survival analysis, medicine, reliability analysis, hydrology, actuarial science, operational research, meteorology, extreme values, quality control, engineering, finance, sports and economics. The total 100 data sets are documented along with associated references for further details and uses.

Find, visualize and explore patterns of differential taxa in vegetation data (namely in a phytosociological table), using the Differential Value (DiffVal). Patterns are searched through mathematical optimization algorithms. Ultimately, Total Differential Value (TDV) optimization aims at obtaining classifications of vegetation data based on differential taxa, as in the traditional geobotanical approach (Monteiro-Henriques 2025, <doi:10.3897/VCS.140466>). The Gurobi optimizer, as well as the R package gurobi', can be installed from <https://www.gurobi.com/products/gurobi-optimizer/>. The useful vignette Gurobi Installation Guide, from package prioritizr', can be found here: <https://prioritizr.net/articles/gurobi_installation_guide.html>.

This package contains a single function dclust() for divisive hierarchical clustering based on recursive k-means partitioning (k = 2). Useful for clustering large datasets where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records). For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.

This package provides a basic, clear implementation of tree-based gradient boosting designed to illustrate the core operation of boosting models. Tuning parameters (such as stochastic subsampling, modified learning rate, or regularization) are not implemented. The only adjustable parameter is the number of training rounds. If you are looking for a high performance boosting implementation with tuning parameters, consider the xgboost package.

This package provides a convenient framework to simulate, test, power, and visualize data for differential expression studies with lognormal or negative binomial outcomes. Supported designs are two-sample comparisons of independent or dependent outcomes. Power may be summarized in the context of controlling the per-family error rate or family-wise error rate. Negative binomial methods are described in Yu, Fernandez, and Brock (2017) <doi:10.1186/s12859-017-1648-2> and Yu, Fernandez, and Brock (2020) <doi:10.1186/s12859-020-3541-7>.

This package provides functions to download, process, and visualize German geospatial data across administrative levels, including states, districts, and municipalities. Supports interactive tables and customized maps using built-in or external datasets. Official shapefiles are accessed from the German Federal Agency for Cartography and Geodesy (BKG) <https://gdz.bkg.bund.de/>, licensed under dl-de/by-2-0 <https://www.govdata.de/dl-de/by-2-0>.

Enhancing cross-language compatibility within the RStudio environment and supporting seamless language understanding, the deepRstudio package leverages the power of the DeepL API (see <https://www.deepl.com/docs-api>) to enable seamless, fast, accurate, and affordable translation of code comments, documents, and text. This package offers the ability to translate selected text into English (EN), as well as from English into various languages, namely Japanese (JA), Chinese (ZH), Spanish (ES), French (FR), Russian (RU), Portuguese (PT), and Indonesian (ID). With much of the text being written in English, the emphasis is on compatibility from English. It is also designed for developers working on multilingual projects and data analysts collaborating with international teams, simplifying the translation process and making code more accessible and comprehensible to people with diverse language backgrounds. This package uses the rstudioapi package and DeepL API, and is simply implemented, executed from addins or via shortcuts on RStudio'. With just a few steps, content can be translated between supported languages, promoting better collaboration and expanding the global reach of work. The functionality of this package works only on RStudio using rstudioapi'.

Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) <doi:10.1016/j.apm.2013.05.038>; Nagy et al. (2011) <doi:10.1002/acs.1239>.

This package provides a framework to help construct R data packages in a reproducible manner. Potentially time consuming processing of raw data sets into analysis ready data sets is done in a reproducible manner and decoupled from the usual R CMD build process so that data sets can be processed into R objects in the data package and the data package can then be shared, built, and installed by others without the need to repeat computationally costly data processing. The package maintains data provenance by turning the data processing scripts into package vignettes, as well as enforcing documentation and version checking of included data objects. Data packages can be version controlled on GitHub', and used to share data for manuscripts, collaboration and reproducible research.

This package provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. The residuals are assumed to follow a skewed T distribution, a skewed normal distribution, or a normal distribution. More details can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.

Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.

The recovery of visual sensitivity in a dark environment is known as dark adaptation. In a clinical or research setting the recovery is typically measured after a dazzling flash of light and can be described by the Mahroo, Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take dark adaptation data and use nonlinear regression to find the parameters of the model that best describe the data. They do this by firstly, generating rapid initial objective estimates of data adaptation parameters, then a multi-start algorithm is used to reduce the possibility of a local minimum. There is also a bootstrap method to calculate parameter confidence intervals. The functions rely upon a dark list or object. This object is created as the first step in the workflow and parts of the object are updated as it is processed.

Utilities for converting unstructured electronic prescribing instructions into structured medication data. Extracts drug dose, units, daily dosing frequency and intervals from English-language prescriptions. Based on Karystianis et al. (2015) <doi:10.1186/s12911-016-0255-x>.

Includes functions that researchers or practitioners may use to clean raw data, transferring html, xlsx, txt data file into other formats. And it also can be used to manipulate text variables, extract numeric variables from text variables and other variable cleaning processes. It is originated from a author's project which focuses on creative performance in online education environment. The resulting paper of that study will be published soon.

This package provides a collection of functions to perform Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA). This package implements the results presented in Prass, T.S. and Pumi, G. (2019). "On the behavior of the DFA and DCCA in trend-stationary processes" <arXiv:1910.10589>.

Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>.

Bayesian inference algorithms based on the population-based "differential evolution" (DE) algorithm. Users can obtain posterior mode (MAP) estimates via DEMAP, posterior samples via DEMCMC, and variational approximations via DEVI.

This package provides functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).

This package provides a collection of functions that allows for easy and consistent use of environment variables. This includes setting, checking, retrieving, transforming, and validating values stored in environment variables.

The Delphi Epidata API provides real-time access to epidemiological surveillance data for influenza, COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron Rumack', Ryan J. Tibshirani', Roni Rosenfeld (2015). Delphi Epidata API. <https://github.com/cmu-delphi/delphi-epidata>.

Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. The package aims to reproduce the results obtained with the Stata's user written opreg <http://www.stata-journal.com/article.html?article=st0145> and levpet <http://www.stata-journal.com/article.html?article=st0060> commands. The first was originally proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>. The second by Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>. And the third by Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>.

This package provides functions to create simulated time series of environmental exposures (e.g., temperature, air pollution) and health outcomes for use in power analysis and simulation studies in environmental epidemiology. This package also provides functions to evaluate the results of simulation studies based on these simulated time series. This work was supported by a grant from the National Institute of Environmental Health Sciences (R00ES022631) and a fellowship from the Colorado State University Programs for Research and Scholarly Excellence.