R-side code to implement an R editor and IDE in Komodo IDE with the SciViews-K extension.

The Structural Topic and Sentiment-Discourse (STS) model allows researchers to estimate topic models with document-level metadata that determines both topic prevalence and sentiment-discourse. The sentiment-discourse is modeled as a document-level latent variable for each topic that modulates the word frequency within a topic. These latent topic sentiment-discourse variables are controlled by the document-level metadata. The STS model can be useful for regression analysis with text data in addition to topic modelingâ s traditional use of descriptive analysis. The method was developed in Chen and Mankad (2024) <doi:10.1287/mnsc.2022.00261>.

This package implements the s-values proposed by Ed. Leamer. It provides a context-minimal approach for sensitivity analysis using extreme bounds to assess the sturdiness of regression coefficients.

Hail is an open-source, general-purpose, python based data analysis tool with additional data types and methods for working with genomic data, see <https://hail.is/>. Hail is built to scale and has first-class support for multi-dimensional structured data, like the genomic data in a genome-wide association study (GWAS). Hail is exposed as a python library, using primitives for distributed queries and linear algebra implemented in scala', spark', and increasingly C++'. The sparkhail is an R extension using sparklyr package. The idea is to help R users to use hail functionalities with the well-know tidyverse syntax, see <https://www.tidyverse.org/>.

This package provides efficient R and C++ routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.

Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

This package provides tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385>, Wilson (2025) <DOI:10.1002/mrm.30462> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

Fitting a smooth path to a given set of noisy spherical data observed at known time points. It implements a piecewise geodesic curve fitting method on the unit sphere based on a velocity-based penalization scheme. The proposed approach is implemented using the Riemannian block coordinate descent algorithm. To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J. K., & Koo, J. Y. (2023) <doi:10.1080/02664763.2022.2054962> for the case of order 1. Additionally, this package includes various functions necessary for handling spherical data.

Capable of deriving seasonal statistics, such as "normals", and analysis of seasonal data, such as departures. This package also has graphics capabilities for representing seasonal data, including boxplots for seasonal parameters, and bars for summed normals. There are many specific functions related to climatology, including precipitation normals, temperature normals, cumulative precipitation departures and precipitation interarrivals. However, this package is designed to represent any time-varying parameter with a discernible seasonal signal, such as found in hydrology and ecology.

This package provides a minimal-dependency R client for OpenAI'-compatible speech-to-text APIs (see <https://platform.openai.com/docs/api-reference/audio>) with optional local fallbacks. Supports OpenAI', local servers, and the whisper package for local transcription.

This package provides a set of Rmarkdown themes for creating scientific and professional documents. Simple interface with features to ease navigation across the page and sub-pages.

Fast k-nearest neighbors (K-NN) and principal component analysis (PCA) imputation algorithms for missing values in epigenetic data or other high-dimensional numeric matrices. For PCA, a locally optimal block preconditioned conjugate gradient (LOBPCG) eigensolver with warm starts of both the eigenblock and search direction is also supported. Two complementary imputation strategies are available. Group-wise imputation (e.g., by chromosome) is recommended for Illumina DNA methylation microarrays (e.g., 450K, EPIC) and other matrices with groupable columns. A sliding window approach for K-NN or PCA imputation is recommended only for whole-genome methylation data such as whole-genome bisulfite sequencing (WGBS) or Enzymatic Methyl-seq (EM-seq). The package also supports hyperparameter tuning via repeated cross-validation. The K-NN algorithm is described in: Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and Botstein, D. (1999) "Imputing Missing Data for Gene Expression Arrays". The PCA imputation is an optimized reimplementation of the imputePCA() function from the missMDA package described in: Josse, J. and Husson, F. (2016) <doi:10.18637/jss.v070.i01> "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis".

This package implements the forward-backward sweep algorithm for computing Nash equilibrium contact policies in SEIR epidemic mean-field games on heterogeneous contact networks, as described in Wang (2026) <doi:10.5281/zenodo.19381052>. Supports both heterogeneous networks with arbitrary degree distributions (e.g., truncated Poisson) and homogeneous networks. Computes equilibrium susceptible contact effort, value functions, epidemic trajectories, and the effective reproduction number Rt.

This package implements estimation methods for shrinkage covariance matrices using user-specified covariance targets. The covariance target is a structured matrix towards which the unbiased sample covariance is shrunk, optionally incorporating prior knowledge. Shrinkage intensity is computed analytically. The method is described and applied to microarray gene expression data in Jelizarow et al. (2010) <doi:10.1093/bioinformatics/btq323>.

Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.

This package implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included.

Provide a workflow to jointly embed chromatin accessibility peaks and expressed genes into a shared low-dimensional space using paired single-cell ATAC-seq (scATAC-seq) and single-cell RNA-seq (scRNA-seq) data. It integrates regulatory relationships among peak-peak interactions (via Cicero'), peak-gene interactions (via Lasso, random forest, and XGBoost), and gene-gene interactions (via principal component regression). With the input of paired scATAC-seq and scRNA-seq data matrices, it assigns a low-dimensional feature vector to each gene and peak. Additionally, it supports the reconstruction of gene-gene network with low-dimensional projections (via epsilon-NN) and then the comparison of the networks of two conditions through manifold alignment implemented in scTenifoldNet'. See <doi:10.1093/bioinformatics/btaf483> for more details.

This package performs canonical correlation for survey data, including multiple tests of significance for secondary canonical correlations. A key feature of this package is that it incorporates survey data structure directly in a novel test of significance via a sequence of simple linear regression models on the canonical variates. See reference - Cruz-Cano, Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" The R Journal under review.

Fitting and plotting parametric or non-parametric size-biased non-negative distributions, with optional covariates if parametric. Rowcliffe, M. et al. (2016) <doi:10.1002/rse2.17>.

This package provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data.

This package provides methods to calculate sample size for single-arm survival studies using the arcsine transformation, incorporating uniform accrual and exponential survival assumptions. Includes functionality for detailed numerical integration and simulation. This method is based on Nagashima et al. (2021) <doi:10.1002/pst.2090>.

An Optimization Algorithm Applied to Stratification Problem.This function aims at constructing optimal strata with an optimization algorithm based on a global optimisation technique called Biased Random Key Genetic Algorithms.

This package provides functions to implement the stability controlled quasi-experiment (SCQE) approach to study the effects of newly adopted treatments that were not assigned at random. This package contains tools to help users avoid making statistical assumptions that rely on infeasible assumptions. Methods developed in Hazlett (2019) <doi:10.1002/sim.8717>.

This package provides two main functionalities. 1 - Given a system of simultaneous equation, it decomposes the matrix of coefficients weighting the endogenous variables into three submatrices: one includes the subset of coefficients that have a causal nature in the model, two include the subset of coefficients that have a interdependent nature in the model, either at systematic level or induced by the correlation between error terms. 2 - Given a decomposed model, it tests for the significance of the interdependent relationships acting in the system, via Maximum likelihood and Wald test, which can be built starting from the function output. For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and Faliva and Zoia (1994) <doi:10.1007/BF02589041>.