This package implements S3 classes for storing dates and date-times based on the Jalali calendar. The main design goal of shide is consistency with base R's Date and POSIXct'. It provide features such as: date-time parsing, formatting and arithmetic.

Analyzing soil food webs or any food web measured at equilibrium. The package calculates carbon and nitrogen fluxes and stability properties using methods described by Hunt et al. (1987) <doi:10.1007/BF00260580>, de Ruiter et al. (1995) <doi:10.1126/science.269.5228.1257>, Holtkamp et al. (2011) <doi:10.1016/j.soilbio.2010.10.004>, and Buchkowski and Lindo (2021) <doi:10.1111/1365-2435.13706>. The package can also manipulate the structure of the food web as well as simulate food webs away from equilibrium and run decomposition experiments.

Enables the complete removal of various Shiny components, such as inputs, outputs and modules. It also aids in the removal of observers that have been created in dynamically created modules.

This package provides a convenient interface to the staticrypt by Robin Moisson <https://github.com/robinmoisson/staticrypt>---'Node.js package for adding a password protection layer to static HTML pages. This package can be integrated into the post-render process of quarto documents to secure them with a password.

Monte Carlo simulations of a game-theoretic model for the legal exemption system of the European cartel law are implemented in order to estimate the (mean) deterrent effect of this system. The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) <doi:10.1515/bejeap-2017-0235>.

The StockDistFit package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) <https://www.jstage.jst.go.jp/article/jappstat/37/1/37_1_1/_pdf/-char/ja> and Benth et al. (2008) <https://books.google.co.ke/books?hl=en&lr=&id=MHNpDQAAQBAJ&oi=fnd&pg=PR7&dq=Stochastic+modeling+of+commodity+prices+using+the+Variance+Gamma+(VG)+model.+&ots=YNIL2QmEYg&sig=XZtGU0lp4oqXHVyPZ-O8x5i7N3w&redir_esc=y#v=onepage&q&f=false>.

This package provides a tool to interactively explore the embeddings created by dimension reduction methods such as Principal Components Analysis (PCA), Multidimensional Scaling (MDS), T-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) or any other.

Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to lowess for smoothing and strucchange for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection.

This package creates static / animated / interactive visualisations embeddable in R Markdown documents. It implements an R-to-JavaScript transpiler and enables users to write JavaScript applications using the syntax of R.

This package implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.

This package provides an interface to search, read, query, and retrieve metadata for datasets hosted on Socrata open data portals. Supports all Socrata data types, including spatial data returned as sf objects.

Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.

Package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.

Computes a simple blinding index for randomized controlled trials introduced in Petroff, Bacak, Dagres, Dilk, Wachter: A simple blinding index for randomized controlled trials. Contemp Clin Trials Commun. 2024 Nov 26;42:101393. <doi:10.1016/j.conctc.2024.101393>. PMID: 39686958.

Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1).

This package provides a wrapper for sparse VAR (Vector Autoregression) and VECM (Vector Error Correction Model) time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Basu and Michailidis (2015) <doi:10.1214/15-AOS1315>.

This package provides functions that wrap HTML Bootstrap components code to enable the design and layout of informative landing home pages for Shiny applications. This can lead to a better user experience for the users and writing less HTML for the developer.

Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).

Formulas for calculating sound velocity, water pressure, depth, density, absorption and sonar equations.

This package provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage C++ for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.

This package provides a suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics)<https://github.com/Lujun995/DiSC_Replication_Code>.

This package provides a database-independent JDBC interface.

Mosaic diagram, scatterplot matrix, Andrews curves, parallel coordinate diagram, radar diagram, and Chernoff plots as a Shiny app, which allow the order of variables to be changed interactively. The apps are intended as teaching examples.

The sparseMatEst package provides functions for estimating sparse covariance and precision matrices with error control. A false positive rate is fixed corresponding to the probability of falsely including a matrix entry in the support of the estimator. It uses the binary search method outlined in Kashlak and Kong (2019) <arXiv:1705.02679> and in Kashlak (2019) <arXiv:1903.10988>.