Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.

This package implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).

Generator and density function for the Generalized Inverse Gaussian (GIG) distribution.

Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.

This package provides tools to build and work with bilateral generalized-mean price indexes (and by extension quantity indexes), and indexes composed of generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS). Covers the core mathematical machinery for making bilateral price indexes, computing price relatives, detecting outliers, and decomposing indexes, with wrappers for all common (and many uncommon) index-number formulas. Implements and extends many of the methods in Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007, <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020, <doi:10.5089/9781484354841.069>).

Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours). Documentation about gRc is provided in the paper by Hojsgaard and Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).

This package provides functions for estimating a generalized partial linear model, a semiparametric variant of the generalized linear model (GLM) which replaces the linear predictor by the sum of a linear and a nonparametric function.

Data sets used in the book Marra and Radice (2025, ISBN:9781032973111) "Copula Additive Distributional Regression Using R", for illustrating the fitting of various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations errors association.

Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.

Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.

An interactive git user interface from the R command line. Intuitive tools to make commits, branches, remotes, and diffs an integrated part of R coding. Built on git2r, a system installation of git is not required and has default on-premises remote option.

Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, <DOI:10.1371/journal.pgen.1008766>.

This package provides tools to run genome-wide association study (GWAS) and genome-wide by environment interaction study (GWEIS) scans using the genetic data stored in a binary dosage file. The user provides a data frame with the subject's covariate data and the information about the binary dosage file returned by the BinaryDosage::getbdinfo() routine.

We provides functions that employ splines to estimate generalized partially linear single index models (GPLSIM), which extend the generalized linear models to include nonlinear effect for some predictors. Please see Y. (2017) at <doi:10.1007/s11222-016-9639-0> and Y., and R. (2002) at <doi:10.1198/016214502388618861> for more details.

This package provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.

Build Open Geospatial Consortium GeoPackage files (<https://www.geopackage.org/>). GDAL utilities for reading and writing spatial data are provided by the terra package. Additional GeoPackage and SQLite features for attributes and tabular data are implemented with the RSQLite package.

The American Community Survey (ACS) <https://www.census.gov/programs-surveys/acs> offers geodatabases with geographic information and associated data of interest to researchers in the area. The goal of this package is to generate objects that allow us to access and consult the information available in various formats, such as in GeoPackage format or in multidimensional ROLAP (Relational On-Line Analytical Processing) star format.

This package provides a variety of multivariable data summary statistics and constructions have been proposed, either to generalize univariable analogs or to exploit multivariable properties. Notable among these are the bivariate peelings surveyed by Green (1981, ISBN:978-0-471-28039-2), the bag-and-bolster plots proposed by Rousseeuw &al (1999) <doi:10.1080/00031305.1999.10474494>, and the minimum spanning trees used by Jolliffe (2002) <doi:10.1007/b98835> to represent high-dimensional relationships among data in a low-dimensional plot. Additionally, biplots of singular value--decomposed tabular data, such as from principal components analysis, make use of vectors, calibrated axes, and other representations of variable elements to complement point markers for case elements; see Gabriel (1971) <doi:10.1093/biomet/58.3.453> and Gower & Harding (1988) <doi:10.1093/biomet/75.3.445> for original proposals. Because they treat the abscissa and ordinate as commensurate or the data elements themselves as point masses or unit vectors, these multivariable tools can be thought of as belonging to geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. gggda extends Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics with statistical transformation ("stat") and geometric construction ("geom") layers for many of these tools, as well as convenience coordinate systems to emphasize intrinsic geometry of the data.

Selected utilities, in particular geoms and stats functions, extending the ggplot2 package. This package imports functions from EnvStats <doi:10.1007/978-1-4614-8456-1> by Millard (2013), ggpp <https://CRAN.R-project.org/package=ggpp> by Aphalo et al. (2023) and ggstats <doi:10.5281/zenodo.10183964> by Larmarange (2023), and then exports them. This package also contains modified code from ggquickeda <https://CRAN.R-project.org/package=ggquickeda> by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions to plots. All functions are tested to make sure that they work reliably.

Integrates game theory and ecological theory to construct social-ecological models that simulate the management of populations and stakeholder actions. These models build off of a previously developed management strategy evaluation (MSE) framework to simulate all aspects of management: population dynamics, manager observation of populations, manager decision making, and stakeholder responses to management decisions. The newly developed generalised management strategy evaluation (GMSE) framework uses genetic algorithms to mimic the decision-making process of managers and stakeholders under conditions of change, uncertainty, and conflict. Simulations can be run using gmse(), gmse_apply(), and gmse_gui() functions.

This package provides tools for sparse regression modelling with grouped predictors using the group subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver near optimal estimates. The group subset penalty can be combined with a group lasso or ridge penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping groups.

This package provides a fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation. Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) <url:<https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2F17-AOS1652 >>. Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) <doi:10.48550/arXiv.2008.02904>. Reinhard Furrer and Roman Flury and Florian Gerber (2022) <url:<https://CRAN.R-project.org/package=spam >>.

Flexible and robust estimation and inference of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2019) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time. See <doi:10.32614/RJ-2021-057> for an overview of the package.

This package provides functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.