Constructs dynamic optimal shrinkage estimators for the weights of the global minimum variance portfolio which are reconstructed at given reallocation points as derived in Bodnar, Parolya, and Thorsén (2021) (<arXiv:2106.02131>). Two dynamic shrinkage estimators are available in this package. One using overlapping samples while the other use nonoverlapping samples.

Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using the vegan package and native R code. Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca(). The second step uses wrda() but vegan::rda() if the site weights are equal. This version has a predict() function. For details see ter Braak et al. 2018 <doi:10.1007/s10651-017-0395-x>. and ter Braak & van Rossum 2025 <doi:10.1016/j.ecoinf.2025.103143>.

Allows humanitarian community, academia, media, government, and non-governmental organizations to utilize the data collected by the Displacement Tracking Matrix (<https://dtm.iom.int>), a unit in the International Organization for Migration. This also provides non-sensitive Internally Displaced Person figures, aggregated at the country, Admin 1 (states, provinces, or equivalent), and Admin 2 (smaller administrative areas) levels.

Access the Google Data Commons API V2 <https://docs.datacommons.org/api/rest/v2/>. Data Commons provides programmatic access to statistical and demographic data from dozens of sources organized in a knowledge graph.

This package provides a framework for the identification and analysis of Differentially Expressed Single Nucleotide Polymorphisms (deSNPs) using high-throughput sequencing data. It enables users to import SNP count data from variant files, perform allele-specific read count extraction, and statistically detect SNPs showing significant differences in allele expression between biological conditions or sample groups. This package contains tools for calculating SNP-index and Delta SNP-index from VCF-derived allele depth data with statistical testing and filtering, including sliding-window analysis of genomic regions.

Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.

Calculate and analyze ecological connectivity across the watercourse of river networks using the Dendritic Connectivity Index.

Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" <doi:10.48550/arXiv.2510.08438>.

This package provides methods for efficient algebraic operations and factorization of dyadic matrices using Rcpp and RcppArmadillo'. The details of dyadic matrices and the corresponding methodology are described in Kos, M., Podgórski, K., and Wu, H. (2025) <doi:10.48550/arXiv.2505.08144>.

This package provides functions for estimating parameters and hyperparameters of the linearized hyperbolic model, and testing equality of hyperparameters.

This package provides novel dendroclimatological methods, primarily used by the Tree-ring research community. There are four core functions. The first one is daily_response(), which finds the optimal sequence of days that are related to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), which implements partial correlations in the analysis of daily response functions. For the enthusiast of monthly data, there is monthly_response() function. The last core function is compare_methods(), which effectively compares several linear and nonlinear regression algorithms on the task of climate reconstruction.

Downloads the public data available from the Brazilian Access to Information Law and and performs a search on information requests and appeals made since 2015.

Access data sets for demonstrating or testing diagnostic classification models. Simulated data sets can be used to compare estimated model output to true data-generating values. Real data sets can be used to demonstrate real-world applications of diagnostic models.

This package provides S4 classes and methods for storing dense and sparse matrices in DuckDB databases. The package supports constructing database-backed matrices from base R and Matrix objects, extracting slices and summaries, performing arithmetic and selected linear algebra operations, and materializing results for larger-than-memory workflows. It integrates with dbProject to keep database paths, live connections, and lazy matrix tables synchronized across interactive analyses.

Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.

Re-arranges a dendrogram to optimize visualisation-based cost functions. The methods implemented here are described in "Advances in Dendrogram Seriation for Application to Visualization", Journal of Computational and Graphical Statistics (2015) D. Earle and C.B. Hurley <doi:10.1080/10618600.2013.874295>.

Dynamic simulations and graphical depictions of autoregressive relationships.

This package provides a Graphical User Interface (GUI) to import, save, detrend and perform standard tree-ring analyses. The interactive detrending allows the user to check how well the detrending curve fits each time-series and change it when needed.

The df2yaml aims to simplify the process of converting dataframe to YAML <https://yaml.org/>. The dataframe with multiple key columns and one value column will be converted to the multi-level hierarchy.

This package provides robustness checks to align estimands with the identification that they require. Given a dagitty object and a model specification, DAGassist classifies variables by causal roles, recovers a target estimand, and generates a report comparing the original model with DAG-derived adjustment sets. Exports publication-grade reports in LaTeX', Word', Excel', dotwhisker', or plain text/'markdown'. DAGassist is built on dagitty', an R package that uses the DAGitty web tool (<https://dagitty.net/>) for creating and analyzing DAGs. Methods draw on Pearl (2009) <doi:10.1017/CBO9780511803161> and Textor et al. (2016) <doi:10.1093/ije/dyw341>.

Compares two dataframes with a common key and returns the delta records. The package will return three dataframes that contain the added, changed, and deleted records.

This package provides the user with an interactive application which can be used to facilitate the planning of dose finding studies by applying the theory of optimal experimental design.

This package provides a function for plotting maps of agricultural field experiments that are laid out in grids. See Ryder (1981) <doi:10.1017/S0014479700011601>.

This package implements the dynamically weighted modified maximum likelihood ridge (DWMMLR) regression estimator, a robust and multicollinearity-aware linear regression estimator that combines the DWMML3 weighting procedure of Sazak (2019) <doi:10.1080/00949655.2019.1571060> with ridge penalization to address both outlier sensitivity and variance inflation due to multicollinearity. The ridge parameter is selected automatically using the approach implemented in the ridgregextra package (Karadag, Sazak, and Aydin, 2023) <https://CRAN.R-project.org/package=ridgregextra>, described further in Karadag, Sazak, and Aydin (2026) <doi:10.1080/02664763.2026.2655681>, which targets a variance inflation factor (VIF) close to but not below 1, removing the need for manual tuning. Returns comprehensive outputs (coefficients, fitted values, residuals, mean squared error (MSE), standard errors, R-squared, and adjusted R-squared) through a simple x/y interface.