Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.

Set of tools aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them.

This package provides the Saylac Shiny application for multidisciplinary analysis of global, national, and regional indicators observed across places and years. SAYLAC abbreviates Spatial Analysis of Yearly, Longitudinal, and Areal Change. The platform supports spatial diagnostics, longitudinal data exploration, time-series diagnostics, comparative forecasting, and automated reporting for development, health, education, economic, environmental, and social indicators. The application implements three connected modules: Spatial Analysis Workflow for exploratory spatial data analysis, Single Model Diagnostics for stationarity testing, trend diagnostics, and comparative forecasting, and Spatial Uncertainty and Reporting Analysis Dashboard for forecast mapping, spatial clustering, and reporting. The application supports choropleth mapping, Moran's I, Geary's C, Local Indicators of Spatial Association, Getis-Ord Gi star statistics, spatial correlograms, Theil-Sen trend estimation, Mann-Kendall testing, Autoregressive Integrated Moving Average models, Exponential Smoothing State Space models, neural network autoregression, BATS, TBATS, theta forecasting, symmetric mean absolute percentage error model comparison, and report generation. The platform was first applied in Touryare and Mohamud (2026) <doi:10.1007/s43621-026-04022-x> for integrated spatial-temporal forecasting of educational attainment in Eastern Africa toward Sustainable Development Goal 4.

Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of the Marker and Time-to-event variables.

An implementation of the stratification index proposed by Zhou (2012) <DOI:10.1177/0081175012452207>. The package provides two functions, srank, which returns stratum-specific information, including population share and average percentile rank; and strat, which returns the stratification index and its approximate standard error. When a grouping factor is specified, strat also provides a detailed decomposition of the overall stratification into between-group and within-group components.

Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing <doi:10.1109/TSP.2016.2605073>.

Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.

Easily create pretty popup messages (modals) in Shiny'. A modal can contain text, images, OK/Cancel buttons, an input to get a response from the user, and many more customizable options.

Generates synthetic ADaM (Analysis Data Model) datasets from real clinical trial data. Preserves the structure of real datasets (column names, value ranges, relationships between treatment and flag columns) while removing identifiable patient information. Supports subject-level (ADSL), longitudinal (BDS), occurrence (OCCDS), and time-to-event (TTE) dataset types.

Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies, following a general simulation workflow that closely aligns with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>. Also includes functions for calculating bootstrap confidence intervals (including normal, basic, studentized, percentile, bias-corrected, and bias-corrected-and-accelerated) with tidy output, as well as for extrapolating confidence interval coverage rates and hypothesis test rejection rates following techniques suggested by Boos and Zhang (2000) <DOI:10.1080/01621459.2000.10474226>.

Bayesian spatial models for survey data, such as Demographic and Health Survey (DHS), with spatial cluster location displacement adjustments. The package implements models for (1) continuous, (2) binary, (3) count and (4) spatially varying models for continuous outcomes. For more details see Bakar et al. (2026) <doi:10.1093/jrsssa/qnag068>.

Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.

This package provides a collection of functions for symbolic computation using the caracas package for structural equation models and other statistical analyses. Among its features is the ability to calculate the model-implied covariance (and correlation) matrix and the sampling covariance matrix of variable functions using the delta method.

Includes general data manipulation functions, algorithms for statistical disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and functions for hierarchical computations by sparse model matrices (Langsrud, 2023) <doi:10.32614/RJ-2023-088>.

Bayesian inference for parametric proportional hazards spatial survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) <doi:10.18637/jss.v077.i04>.

Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup.

This package provides functions for small area estimation.

Estimation of model parameters for marked Hawkes process. Accounts for missing data in the estimation of the parameters. Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).

Implementation of the family of generalised age-period-cohort stochastic mortality models. This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.2307/2290201> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations.

Generates a random quotation from a database of quotes on topics in statistics, data visualization and science. Other functions allow searching the quotes database by key term tags, or authors or creating a word cloud. The output is designed to be suitable for use at the console, in Rmarkdown and LaTeX.

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.

Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.

Search for variables across multiple survey datasets, examine variable properties (labels, values, missingness), and explore variable context within datasets. Useful for navigating complex survey data with many variables and understanding variable relationships and metadata.

This package provides a small collection of data on graduate statistics programs from the United States.