This package provides functions to identify European NUTS (Nomenclature of Territorial Units for Statistics) regions for geographic coordinates (latitude/longitude) using Eurostat geospatial boundaries. Includes map-based visualisation of the matched regions for validation and exploration. Designed for regional data analysis, reproducible workflows, and integration with common geospatial R packages.

This package provides a collection of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and the guilds sampling formula conditioned on guild size (Janzen et al. 2015).

Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. It comes with features to accept data formats in plink(.raw) and VCF. It provides users flexibility to extract and filter individuals, mutations and genes of interest.

This is an add-on package to GAMLSS. The purpose of this package is to allow users to fit interval response variables in GAMLSS models. The main function gen.cens() generates a censored version of an existing GAMLSS family distribution.

Computes Gromov-Hausdorff type l^p distances for labeled metric spaces. These distances were introduced in V.Liebscher, Gromov meets Phylogenetics - new Animals for the Zoo of Metrics on Tree Space <arXiv:1504.05795> for phylogenetic trees, but may apply to a diversity of scenarios.

Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (in press) <doi:10.1016/j.ecosta.2025.09.003>.

GWAS R API Data Download. This package provides easy access to the NHGRI'-'EBI GWAS Catalog data by accessing the REST API <https://www.ebi.ac.uk/gwas/rest/docs/api/>.

An R interface to the GPTZero API (<https://gptzero.me/docs>). Allows users to classify text into human and computer written with probabilities. Formats the data into data frames where each sentence is an observation. Paragraph-level and document-level predictions are organized to align with the sentences.

This package provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

This package provides a gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48.

We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The details of methods can be found in "Tian, Y., & Feng, Y. (2023). Transfer learning under high-dimensional generalized linear models. Journal of the American Statistical Association, 118(544), 2684-2697.".

This package provides a way to log ggplot component calls, which can be useful for debugging and understanding how ggplot objects are created. The logged calls can be printed, saved, and re-executed to reproduce the original ggplot object.

Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.

This package implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm (Robins (1986) <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). The g-formula can estimate an outcome's counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders. This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments. See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.

Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.

Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the graphicalVAR approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. The top level function used to estimate marginal likelihood is called evidence(), which expects the prior name, data, and relevant prior specific parameters. This package also provides an MCMC prior sampler using the same underlying approach, implemented in prior_sampling(), which expects a prior name and prior specific parameters. Both functions also expect the number of burn-in iterations and the number of sampling iterations for the underlying MCMC sampler.

Quickly and easily perform exploratory data analysis by uploading your data as a csv file. Start generating insights using ggplot2 plots and table1 tables with descriptive stats, all using an easy-to-use point and click Shiny interface.

The aim of this package is to offer more variability of graphics based on the self-organizing maps.

This package provides tools implementing an automated version of the graphic double integration technique (GDI) for volume implementation, and some other related utilities for paleontological image-analysis. GDI was first employed by Jerison (1973) <ISBN:9780323141086> and Hurlburt (1999) <doi:10.1080/02724634.1999.10011145> and is primarily used for volume or mass estimation of (extinct) animals. The package gdi aims to make this technique as convenient and versatile as possible. The core functions of gdi provide utilities for automatically measuring diameters from digital silhouettes provided as image files and calculating volume via graphic double integration with simple elliptical, superelliptical (following Motani 2001 <doi:10.1666/0094-8373(2001)027%3C0735:EBMFST%3E2.0.CO;2>) or complex cross-sectional geometries (see also Zhao 2024 <doi:10.7717/peerj.17479>). Additionally, the package provides functions for estimating the center of mass position (COM), the moment of inertia (I) for 3D shapes and the second moment of area (Ix, Iy, Iz) of 2D cross-sections, as well as for the visualization of results.

Set of routines for making map projections (forward and inverse), topographic maps, perspective plots, geological maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.

Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.

Run grass growth simulations using a grass growth model based on ModVege (Jouven, M., P. Carrère, and R. Baumont "Model Predicting Dynamics of Biomass, Structure and Digestibility of Herbage in Managed Permanent Pastures. 1. Model Description." (2006) <doi:10.1111/j.1365-2494.2006.00515.x>). The implementation in this package contains a few additions to the above cited version of ModVege, such as simulations of management decisions, and influences of snow cover. As such, the model is fit to simulate grass growth in mountainous regions, such as the Swiss Alps. The package also contains routines for calibrating the model and helpful tools for analysing model outputs and performance.

This package provides functions for simulating and estimating parameters of various growth models, including Logistic, Exponential, Theta-logistic, Von-Bertalanffy, and Gompertz models. The package supports both simulated and real data analysis, including parameter estimation, visualization, and calculation of global and local estimates. The methods are based on research described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in (<https://www.researchsquare.com/article/rs-2363586/v1>). An interactive web application is also available at [GPEMR Web App](<https://gpem-r.shinyapps.io/GPEM-R/>).