Write SARIMA models in (finite) AR representation and simulate generalized multiplicative seasonal autoregressive moving average (time) series with Normal / Gaussian, Poisson or negative binomial distribution. The methodology of this method is described in Briet OJT, Amerasinghe PH, and Vounatsou P (2013) <doi:10.1371/journal.pone.0065761>.

This package provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The implementation is in C++ and uses RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included.

This package provides a novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.

Give advice about good practices when building R packages. Advice includes functions and syntax to avoid, package structure, code complexity, code formatting, etc.

This package provides a collection of different indices and visualization techniques for evaluate the seed germination process in ecophysiological studies (Lozano-Isla et al. 2019) <doi:10.1111/1440-1703.1275>.

Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around gbm (gradient boosting machine) functions in dismo (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around gbm (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 Working Guide <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows Appendix S3. See <https://www.simondedman.com/> for published guides and papers using this package.

Applies sequential clustering algorithm to animal location data based on user-defined parameters. Plots interactive cluster maps and provides a summary dataframe with attributes for each cluster commonly used as covariates in subsequent modeling efforts. Additional functions provide individual keyhole markup language plots for quick assessment, and export of global positioning system exchange format files for navigation purposes. Methods can be found at <doi:10.1111/2041-210X.13572>.

This package contains five functions performing the calculation of unconditional and conditional Granger-causality spectra, bootstrap inference on both, and inference on the difference between them via the bootstrap approach of Farne and Montanari, 2018 <arXiv:1803.00374>.

Data sets used in the book "R Graphics Cookbook" by Winston Chang, published by O'Reilly Media.

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 methods for calculating gradient surface metrics for continuous analysis of landscape features.

This package provides a collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

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 a genomic simulation approach for creating biologically informed individual genotypes from empirical data that 1) samples alleles from populations without replacement, 2) segregates alleles based on species-specific recombination rates. gscramble is a flexible simulation approach that allows users to create pedigrees of varying complexity in order to simulate admixed genotypes. Furthermore, it allows users to track haplotype blocks from the source populations through the pedigrees.

In practical applications, the assumptions underlying generalized linear models frequently face violations, including incorrect specifications of the outcome variable's distribution or omitted predictors. These deviations can render the results of standard generalized linear models unreliable. As the sample size increases, what might initially appear as minor issues can escalate to critical concerns. To address these challenges, we adopt a permutation-based inference method tailored for generalized linear models. This approach offers robust estimations that effectively counteract the mentioned problems, and its effectiveness remains consistent regardless of the sample size.

This package provides classes and methods for handling networks or graphs whose nodes are geographical (i.e. locations in the globe). The functionality includes the creation of objects of class geonetwork as a graph with node coordinates, the computation of network measures, the support of spatial operations (projection to different Coordinate Reference Systems, handling of bounding boxes, etc.) and the plotting of the geonetwork object combined with supplementary cartography for spatial representation.

Additional annotations, stats, geoms and scales for plotting "light" spectra with ggplot2', together with specializations of ggplot() and autoplot() methods for spectral data and waveband definitions stored in objects of classes defined in package photobiology'. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

This package provides functions and a graphical user interface for graphical described multiple test procedures.

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>).

This package provides a ggplot2 extension that adds specialised arrow geometry layers. It offers more arrow options than the standard grid arrows that are built-in many line-based geom layers.

This package provides probability density functions and sampling algorithms for three key distributions from the General Unimodal Distribution (GUD) family: the Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t distribution, and the Two-Piece Scale (TPSC) Student-t distribution. Additionally, this package includes a function for Bayesian linear modal regression, leveraging these three distributions for model fitting. The details of the Bayesian modal regression model based on the GUD family can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.

This package provides routines to estimate the Mixture Transition Distribution Model based on Raftery (1985) <http://www.jstor.org/stable/2345788> and Nicolau (2014) <doi:10.1111/sjos.12087> specifications, for multivariate data. Additionally, provides a function for the estimation of a new model for multivariate non-homogeneous Markov chains. This new specification, Generalized Multivariate Markov Chains (GMMC) was proposed by Carolina Vasconcelos and Bruno Damasio and considers (continuous or discrete) covariates exogenous to the Markov chain.

Perform the Blinder-Oaxaca decomposition for generalized linear model with bootstrapped standard errors. The twofold and threefold decomposition are given, even the generalized linear model output in each group.

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 >>.