This package provides a package for inferring, comparing, and visualizing gene networks from single-cell RNA sequencing data. It integrates multiple methods (GENIE3, GRNBoost2, ZILGM, PCzinb, and JRF) for robust network inference, supports consensus building across methods or datasets, and provides tools for evaluating regulatory structure and community similarity. GRNBoost2 requires Python package arboreto which can be installed using init_py(install_missing = TRUE). This package includes adapted functions from ZILGM (Park et al., 2021), JRF (Petralia et al., 2015), and learn2count (Nguyen et al. 2023) packages with proper attribution under GPL-2 license.

Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the nimble package or from other packages. Plug-ins for JAGS via rjags and Stan via rstan are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components.

This package provides functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) <doi:10.1016/j.wace.2018.01.002>.

It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.

This package provides methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (â Equity Asset Valuation (4th Edition)â , 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) <doi:10.3390/econometrics8020020>, details on the software can be found in Knaus et al. (2021) <doi:10.18637/jss.v100.i13>, while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>.

API client for ClimMob', an open source software for decentralized large-N trials with the tricot approach <https://climmob.net/>. Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions for the target environment, it enables many participants to carry out reasonably simple experiments that taken together can offer even more information. ClimMobTools enables project managers to deep explore and analyse their ClimMob data in R.

This package provides a shiny application to automate forward and back survey translation with optional reconciliation using large language models (LLMs). Supports both item-by-item and batch translation modes for optimal performance and context-aware translations. Handles multi-sheet Excel files and supports OpenAI (GPT), Google Gemini, and Anthropic Claude models. Follows the TRAPD (Translation, Review, Adjudication, Pretesting, Documentation) framework and ISPOR (International Society for Pharmacoeconomics and Outcomes Research) recommendations. See Harkness et al. (2010) <doi:10.1002/9780470609927.ch7> and Wild et al. (2005) <doi:10.1111/j.1524-4733.2005.04054.x>.

This package provides a hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package MajMinKmeans are cluster_km() and cluster_MajKm(). Cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Another useful function is MajMinOptim(), which helps to find the optimum values of the Majorization-Minimization estimator.

It leverages the network-like architecture of scientific models together with software quality metrics to identify chains of function calls that are more prone to generating and propagating errors. It operates on tbl_graph objects representing call dependencies between functions (callers and callees) and computes risk scores for individual functions and for paths (sequences of function calls) based on cyclomatic complexity, in-degree and betweenness centrality. The package supports variance-based uncertainty and sensitivity analyses after Puy et al. (2022) <doi:10.18637/jss.v102.i05> to assess how risk scores change under alternative risk definitions.

Several functions and S3 methods for G-computation and emulation of clinical trials. It allows for flexible estimation of the outcome model, especially penalized regressions (Lasso, Ridge, or Elasticnet) for binary, continuous, counting, or right-censored time-to-event outcomes. Average treatment effect among the entire population (ATE) or among the treated population (ATT) can be estimated. The method for time-to-events is described by Chatton et al. (2020) <doi:10.1038/s41598-020-65917-x>. For a binary outcome, details are available in the paper proposed by Chatton et al. (2022) <doi:10.1177/09622802211047345>.

Simulates stochastic hybrid models for transmission of infectious diseases in dynamic networks. It is a metapopulation model in which each node in the network is a sub-population and disease spreads within nodes and among them, combining two approaches: stochastic simulation algorithm (<doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based approach, respectively. Equations that models spread within nodes are customizable and there are two link types among nodes: migration and influence (commuting). More information in Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) <doi:10.18637/jss.v094.i06>.

Frequentist and Bayesian linear regression for large data sets. Useful when the data does not fit into memory (for both frequentist and Bayesian regression), to make running time manageable (mainly for Bayesian regression), and to reduce the total running time because of reduced or less severe memory-spillover into the virtual memory. This is an implementation of Merge & Reduce for linear regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). Streaming statistical models via Merge & Reduce'. International Journal of Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.

This package provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.

This package contains functions to query and visualize the Neuroimaging features associated with genetically regulated gene expression (GReX). The primary utility, neuroimaGene(), relies on a list of user-defined genes and returns a table of neuroimaging features (NIDPs) associated with each gene. This resource is designed to assist in the interpretation of genome-wide and transcriptome-wide association studies that evaluate brain related traits. Bledsoe (2024) <doi:10.1016/j.ajhg.2024.06.002>. In addition there are several visualization functions that generate summary plots and 2-dimensional visualizations of regional brain measures. Mowinckel (2020).

This package provides a roclet for roxygen2 that identifies and processes code blocks in your documentation marked with `@longtests`. These blocks should contain tests that take a long time to run and thus cannot be included in the regular test suite of the package. When you run `roxygen2::roxygenise` with the `longtests_roclet`, it will extract these long tests from your documentation and save them in a separate directory. This allows you to run these long tests separately from the rest of your tests, for example, on a continuous integration server that is set up to run long tests.

glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian".

Interactive gadgets and plotting functions for visualizing data sets and statistical concepts in two and three dimensions. Explore a data frame as a 3D point cloud you can rotate, fit a moderated (interaction) regression and view its surface as a 3D wireframe, or plot principal components and regression fits in two dimensions. Each interactive gadget returns the call that reproduces its final view, including the viewing angle, so an exploratory session can be pasted into a script or report. Also provides simulation-based demonstrations of sampling distributions, confidence intervals, t-tests, and matrix inversion for teaching and self-study.

Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022)<doi:10.1101/2022.06.23.497396>. This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances. The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes.

The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. The Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to the number of lags involved in the test and therefore it should be used with extra caution. The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) <doi:10.1016/j.physa.2018.12.028>.

Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1016/j.jenvman.2020.110248>.

This is an ExperimentHub package that provides access to the data generated and analyzed in the [smoking-nicotine-mouse](https://github.com/LieberInstitute/smoking-nicotine-mouse/) LIBD project. The datasets contain the expression data of mouse genes, transcripts, exons, and exon-exon junctions across 208 samples from pup and adult mouse brain, and adult blood, that were exposed to nicotine, cigarette smoke, or controls. They also contain relevant metadata of these samples and gene expression features, such QC metrics, if they were used after filtering steps and also if the features were differently expressed in the different experiments.

Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>. bibliometrix provides various routines for importing bibliographic data from SCOPUS', Clarivate Analytics Web of Science (<https://www.webofknowledge.com/>), Digital Science Dimensions (<https://www.dimensions.ai/>), OpenAlex (<https://openalex.org/>), Cochrane Library (<https://www.cochranelibrary.com/>), Lens (<https://www.lens.org/>), and PubMed (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.

This package provides high-performance tools for macroeconomic trend extraction and filtering, specifically designed to solve the end-point problem in real-time. Implements the MacroBoost Hybrid (MBH) filter using penalized P-splines and gradient boosting. Unlike the standard Hodrick-Prescott filter, MacroFilters utilizes component-wise L2-boosting with robust loss functions (Huber) to handle extreme transient shocks (e.g., COVID-19) without inducing spurious trend shifts. The algorithm includes an automated two-layer diagnostic stage for unit roots and structural breaks, optimized via corrected AICc for computational efficiency. Methodology detailed in Kinel (2026) <doi:10.2139/ssrn.6371138>.