Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The rjags package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).

Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types functional correspondence even between evolutionarily distant species.

This package provides routines to check identifiability of linear structural equation models and factor analysis models. The routines are based on the graphical representation of structural equation models.

Extends the classical SSIM method proposed by Wang', Bovik', Sheikh', and Simoncelli'(2004) <doi:10.1109/TIP.2003.819861>. for irregular lattice-based maps and raster images. The geographical SSIM method incorporates well-developed geographically weighted summary statistics'('Brunsdon', Fotheringham and Charlton 2002) <doi:10.1016/S0198-9715(01)00009-6> with an adaptive bandwidth kernel function for irregular lattice-based maps.

Utilities for injecting search engine optimization (SEO), Open Graph, Twitter, site verification, and schema.org metadata into Shiny applications from YAML files or named lists.

Access functionality of the heatmaply package through Shiny UI'.

This package provides ggplot2 graphics for analysing time series data. It aims to fit into the tidyverse and grammar of graphics framework for handling temporal data.

This package provides extensions for package sitree for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions.

This package provides helper functions to compute linear predictors, time-dependent ROC curves, and Harrell's concordance index for Cox proportional hazards models as described in Therneau (2024) <https://CRAN.R-project.org/package=survival>, Therneau and Grambsch (2000, ISBN:0-387-98784-3), Hung and Chiang (2010) <doi:10.1002/cjs.10046>, Uno et al. (2007) <doi:10.1198/016214507000000149>, Blanche, Dartigues, and Jacqmin-Gadda (2013) <doi:10.1002/sim.5958>, Blanche, Latouche, and Viallon (2013) <doi:10.1007/978-1-4614-8981-8_11>, Harrell et al. (1982) <doi:10.1001/jama.1982.03320430047030>, Peto and Peto (1972) <doi:10.2307/2344317>, Schemper (1992) <doi:10.2307/2349009>, and Uno et al. (2011) <doi:10.1002/sim.4154>.

This package performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression.

Fits Bayesian linear mixed models for spatial and space-time data with fixed effects, independent and identically distributed (iid) grouped random effects, and structured latent processes. The formula interface supports first-order autoregressive (AR(1)) effects, dense Gaussian processes, nearest-neighbor Gaussian processes, proper and Leroux conditional autoregressive (CAR) effects, ordered directed acyclic graph autoregressive (DAGAR) effects, separable CAR-time and DAGAR-time effects, and spatially varying coefficients. The sampler uses sparse precision matrix calculations when available and includes post-fitting tools for latent process recovery, fitted values, prediction, pointwise log likelihoods, and posterior sample extraction. Method details include Datta et al. (2016) <doi:10.1080/01621459.2015.1044091>, Finley et al. (2019) <doi:10.1080/10618600.2018.1537924>, Datta et al. (2019) <doi:10.1214/19-BA1177>, and May and Finley (2025) <doi:10.1016/j.spasta.2025.100917>.

This package implements the Singularity Regression Kriging ('SRK') model for spatial prediction by integrating covariate singularity feature construction, nonlinear trend estimation via random forest, and geostatistical interpolation of residuals using ordinary kriging. Singularity-based anomaly indices are computed from environmental covariates at multiple spatial scales to capture local multiscale heterogeneity and augment the random forest feature set for trend estimation. The resulting residuals are interpolated using ordinary kriging to generate final spatial predictions with uncertainty quantification. Tools for spatial block cross-validation, parameter sensitivity analysis, and diagnostic visualization are also provided. Methods are based on Ren, Song, Chen, and Yu (2026) <doi:10.1080/15481603.2026.2690341>, with singularity theory from Cheng (2012) <doi:10.1016/j.gexplo.2012.07.007> and Cheng (2017) <doi:10.1016/j.gr.2017.07.011>, random forest methodology from Breiman (2001) <doi:10.1023/A:1010933404324>, and regression kriging framework from Hengl, Heuvelink, and Rossiter (2007) <doi:10.1016/j.cageo.2007.05.001>.

Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, counterfactual analysis, and computation of impulse response functions, generalized impulse response functions, generalized forecast error variance decompositions, as well as historical decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.1016/j.jedc.2025.105162>, Savi Virolainen (2026) <doi:10.1080/07474938.2026.2673986>.

This package provides a programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility ('GBIF'), iNaturalist', eBird', Integrated Digitized Biocollections ('iDigBio'), VertNet', Ocean Biogeographic Information System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes functionality for retrieving species occurrence data, and combining those data.

This package creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. For a description of AdaBoost, see Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included.

Design, build, and deploy R packages demo presentations by an interactive wizard. Set up unique title, logo and themes. Add personalized tabs exposing applicability. And deploy as a part of a package or an independent app.

Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.

This package provides tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.

Allows shiny developers to incorporate UI elements based on Google's Material design. See <https://material.io/guidelines/> for more information.

This package implements multi-study learning algorithms such as merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap, the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights. Embedded within the caret framework, this package allows for a wide range of single-study learners (e.g., neural networks, lasso, random forests). The package offers over 20 default similarity measures and allows for specification of custom similarity measures for covariate-profile similarity weighting and an accept/reject step. This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019) <doi:10.1101/856385>.

The Swiss Ephemeris (version 2.10.03) is a high precision ephemeris based upon the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to 17191 CE. This package uses the semi-analytic theory by Steve Moshier. For faster and more accurate calculations, the compressed Swiss Ephemeris data is available in the swephRdata package. To access this data package, run install.packages("swephRdata", repos = "https://rstub.r-universe.dev", type = "source")'. The size of the swephRdata package is approximately 115 MB. The user can also use the original JPL DE431 data.

Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2024) <doi:10.1016/j.cam.2023.115379>; Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).

Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator.

This package provides the data sets used to exemplify specmine'. These data sets were formerly distributed with specmine', but they exceed current CRAN policy for package size.