Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Templates and data files to support "Discrete Choice Analysis with R", Páez, A. and Boisjoly, G. (2023) <doi:10.1007/978-3-031-20719-8>.

This package provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds" in <doi:10.1098/rsif.2019.0234>. Using the functions in the package, that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data using other functions in this package (as in the manuscript) or from some other source. The package provides a vignette which walks through the analysis in the publication, as well as a function to generate a project skeleton for such an analysis.

This package provides a software package for using DEXi models. DEXi models are hierarchical qualitative multi-criteria decision models developed according to the method DEX (Decision EXpert, <https://dex.ijs.si/documentation/DEX_Method/DEX_Method.html>), using the program DEXi (<https://kt.ijs.si/MarkoBohanec/dexi.html>) or DEXiWin (<https://dex.ijs.si/dexisuite/dexiwin.html>). A typical workflow with DEXiR consists of: (1) reading a .dxi file, previously made using the DEXi software (function read_dexi()), (2) making a data frame containing input values of one or more decision alternatives, (3) evaluating those alternatives (function evaluate()), (4) analyzing alternatives (selective_explanation(), plus_minus(), compare_alternatives()), (5) drawing charts. DEXiR is restricted to using models produced externally by the DEXi software and does not provide functionality for creating and/or editing DEXi models directly in R'.

This package provides a tool for manipulating data using the generic formula. A single formula allows to easily add, replace and remove variables before running the analysis.

Data sets and sample analyses from Jay L. Devore (2008), "Probability and Statistics for Engineering and the Sciences (7th ed)", Thomson.

An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. Gerlovina et al. (2022) <doi:10.1093/genetics/iyac126>.

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.

This package creates the "table one" of bio-medical papers. Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate, bivariate analysis and parse it into HTML. It also allows you to visualize all your data with graphic representation.

This package contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.

This hosts the findRFM function which generates RFM scores on a 1-5 point scale for customer transaction data. The function consumes a data frame with Transaction Number, Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes. The function returns a data frame with RFM data for the sales information.

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>.

Solving large scale distance weighted discrimination. The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <doi:10.48550/arXiv.1604.05473> for more details.

Time-varying coefficient models for interval censored and right censored survival data including 1) Bayesian Cox model with time-independent, time-varying or dynamic coefficients for right censored and interval censored data studied by Sinha et al. (1999) <doi:10.1111/j.0006-341X.1999.00585.x> and Wang et al. (2013) <doi:10.1007/s10985-013-9246-8>, 2) Spline based time-varying coefficient Cox model for right censored data proposed by Perperoglou et al. (2006) <doi:10.1016/j.cmpb.2005.11.006>, and 3) Transformation model with time-varying coefficients for right censored data using estimating equations proposed by Peng and Huang (2007) <doi:10.1093/biomet/asm058>.

This package implements a generalized linear model approach for detecting differentially expressed genes across treatment groups in count data. The package supports both quasi-Poisson and negative binomial models to handle over-dispersion, ensuring robust identification of differential expression. It allows for the inclusion of treatment effects and gene-wise covariates, as well as normalization factors for accurate scaling across samples. Additionally, it incorporates statistical significance testing with options for p-value adjustment and log2 fold range thresholds, making it suitable for RNA-seq analysis as described in by Xu et al., (2024) <doi:10.1371/journal.pone.0300565>.

Dual Wavelet based Nonlinear Autoregressive Distributed Lag model has been developed for noisy time series analysis. This package is designed to capture both short-run and long-run relationships in time series data, while incorporating wavelet transformations. The methodology combines the NARDL model with wavelet decomposition to better capture the nonlinear dynamics of the series and exogenous variables. The package is useful for analyzing economic and financial time series data that exhibit both long-term trends and short-term fluctuations. This package has been developed using algorithm of Jammazi et al. <doi:10.1016/j.intfin.2014.11.011>.

Summarise patient-level drug utilisation cohorts using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. New users and prevalent users cohorts can be generated and their characteristics, indication and drug use summarised.

Allows users to quickly and easily detect data containing Personally Identifiable Information (PII) through convenience functions.

Extremely fast and memory efficient computation of the DER (or PaF) income polarization index as proposed by Duclos J. Y., Esteban, J. and Ray D. (2004). "Polarization: concepts, measurement, estimation". Econometrica, 72(6): 1737--1772. <doi:10.1111/j.1468-0262.2004.00552.x>. The index may be computed for a single or for a range of values of the alpha-parameter and bootstrapping is also available.

Facilitate the analysis of teams in a corporate setting: assess the diversity per grade and job, present the results, search for bias (in hiring and/or promoting processes). It also provides methods to simulate the effect of bias, random team-data, etc. White paper: Philippe J.S. De Brouwer (2021) <http://www.de-brouwer.com/assets/div/div-white-paper.pdf>. Book (chapter 36): Philippe J.S. De Brouwer (2020, ISBN:978-1-119-63272-6) and Philippe J.S. De Brouwer (2020) <doi:10.1002/9781119632757>.

Interface for Rcpp users to dlib <http://dlib.net> which is a C++ toolkit containing machine learning algorithms and computer vision tools. It is used in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. This package allows R users to use dlib through Rcpp'.

DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data. This package is the DataSHIELD interface implementation for Opal', which is the data integration application for biobanks by OBiBa'. Participant data, once collected from any data source, must be integrated and stored in a central data repository under a uniform model. Opal is such a central repository. It can import, process, validate, query, analyze, report, and export data. Opal is the reference implementation of the DataSHIELD infrastructure.

This package contains a robust set of tools designed for constructing deep neural networks, which are highly adaptable with user-defined loss function and probability models. It includes several practical applications, such as the (deepAFT) model, which utilizes a deep neural network approach to enhance the accelerated failure time (AFT) model for survival data. Another example is the (deepGLM) model that applies deep neural network to the generalized linear model (glm), accommodating data types with continuous, categorical and Poisson distributions.

This package provides functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) (<DOI:10.1016/0304-4076(80)90070-6>).