The goal of SIHR is to provide inference procedures in the high-dimensional generalized linear regression setting for: (1) linear functionals <doi:10.48550/arXiv.1904.12891> <doi:10.48550/arXiv.2012.07133>, (2) conditional average treatment effects, (3) quadratic functionals <doi:10.48550/arXiv.1909.01503>, (4) inner product, (5) distance.

Generates binary test data based on Item Response Theory using the two-parameter logistic model (Lord, 1980 <doi:10.4324/9780203056615>). Useful functions for test equating are included, e.g. functions for generating internal and external common items between test forms and a function to create a linkage plans between those forms. Ancillary functions for generating true item and person parameters as well as for calculating the probability of a person correctly answering an item are also included.

Enables drag-and-drop behaviour in Shiny apps, by exposing the functionality of the SortableJS <https://sortablejs.github.io/Sortable/> JavaScript library as an htmlwidget'. You can use this in Shiny apps and widgets, learnr tutorials as well as R Markdown. In addition, provides a custom learnr question type - question_rank() - that allows ranking questions with drag-and-drop.

Handle POST requests on a custom path (e.g., /ingress) inside the same shiny HTTP server using user interface functions and HTTP responses. Expose latest payload as a reactive and provide helpers for query parameters.

This package provides a workflow based on scTenifoldNet to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the geneâ s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the geneâ s function in the analyzed cells.

Store persistent and synchronized data from shiny inputs within the browser. Refresh shiny applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using Dexie.js <https://dexie.org>, a minimal wrapper for IndexedDB'. Transfer browser link parameters to shiny input or output values. Store app visitor views, likes and followers.

This package provides a simple authentification mechanism for single shiny applications. Authentification and password change functionality are performed calling user provided functions that typically access some database backend. Source code of main applications is protected until authentication is successful.

Includes built-in methods for generating long SQL CASE statements, and other SQL statements that may otherwise be arduous to construct by hand.The generated statement can easily be concatenated to string literals to form queries to SQL'-like databases, such as when using the RODBC package. The current methods include casewhen() for building CASE statements, inlist() for building IN statements, and updatetable() for building UPDATE statements.

Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. stacks implements a grammar for tidymodels'-aligned model stacking.

This package provides a unifying framework for managing and deploying shiny applications that consist of modules, where an "app" is a tab-based workflow that guides a user step-by-step through an analysis. The shinymgr app builder "stitches" shiny modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Users of shinymgr apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an R Markdown report for rapid reporting. In short, developers use the shinymgr framework to write modules and seamlessly combine them into shiny apps, and users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination.

This package provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects.

This package provides a major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().

An extensible framework for developing species distribution models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time. For more information, please check the following paper: Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.

Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) <doi:10.18637/jss.v103.i05>, Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) <doi:10.1080/10618600.2018.1537924>, and Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.

Process and summarize DAS data files. These files are typically, but do not have to be DAS <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data produced by the Southwest Fisheries Science Center (SWFSC) program WinCruz'. This package standardizes and streamlines basic DAS data processing, and includes a PDF with the DAS data format requirements expected by the package.

This package produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold regression analysis results from several models side-by-side, as well as summary statistics.

Framework to build an individual tree simulator.

This package provides a network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.

Calculation of solar zenith and azimuth angles.

This package contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.

S4 class wrappers for the ODBC and Pool DBI connection, also provides some utilities to paste small datasets to clipboard, rename columns. It is used by the package stacomiR for connections to the database. Development versions of stacomiR are available in R-forge.

The Swiss Ephemeris (version 2.10.03) is a high precision ephemeris based upon the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to 17191 CE. This package uses the semi-analytic theory by Steve Moshier. For faster and more accurate calculations, the compressed Swiss Ephemeris data is available in the swephRdata package. To access this data package, run install.packages("swephRdata", repos = "https://rstub.r-universe.dev", type = "source")'. The size of the swephRdata package is approximately 115 MB. The user can also use the original JPL DE431 data.

An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.

Automatically fetch, transform and arrange subsets of multidimensional data sets (collections of files) stored in local and/or remote file systems or servers, using multicore capabilities where possible. This tool provides an interface to perceive a collection of data sets as a single large multidimensional data array, and enables the user to request for automatic retrieval, processing and arrangement of subsets of the large array. Wrapper functions to add support for custom file formats can be plugged in/out, making the tool suitable for any research field where large multidimensional data sets are involved.