This package provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. The package also provides an interface to the SuiteSparse Matrix Collection, <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix benchmarks collected from a wide range of applications.

Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2018) <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 <https://journal.r-project.org/archive/2021/RJ-2021-057/RJ-2021-057.pdf> for an overview of the package.

This package implements the GAMbag, GAMrsm and GAMens ensemble classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601> , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs) as base classifiers. Once an ensemble classifier has been trained, it can be used for predictions on new data. A function for cross validation is also included.

Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to lme4 (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and PLmixed (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).

This package provides a collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at <http://hdl.handle.net/11299/204551>, and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's Struggle for Existence in the journal Ecology and Evolution.

Generates a variety of structured test matrices commonly used in numerical linear algebra and computational experiments. Includes well-known matrices for benchmarking and testing the performance, stability, and accuracy of linear algebra algorithms. Inspired by MATLAB gallery functions.

This package provides tools to fill missing values in satellite data and to develop new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. The package is illustrated with MODIS NDVI data.

This package performs genetic algorithm (Scrucca, L (2013) <doi:10.18637/jss.v053.i04>) assisted genomic best liner unbiased prediction for genomic selection. It also provides a binning method in natural population for genomic selection under the principle of linkage disequilibrium for dimensional reduction.

This package provides functions for estimating a GARCHSK model and GJRSK model based on a publication by Leon et,al (2005)<doi:10.1016/j.qref.2004.12.020> and Nakagawa and Uchiyama (2020)<doi:10.3390/math8111990>. These are a GARCH-type model allowing for time-varying volatility, skewness and kurtosis.

Fits generalized additive models for the location, scale and shape parameters of a generalized extreme value response distribution. The methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005), <doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from the gamlss package <doi:10.32614/CRAN.package.gamlss>.

Finds adaptive strategies for sequential symmetric games using a genetic algorithm. Currently, any symmetric two by two matrix is allowed, and strategies can remember the history of an opponent's play from the previous three rounds of moves in iterated interactions between players. The genetic algorithm returns a list of adaptive strategies given payoffs, and the mean fitness of strategies in each generation.

Collection of packages for work with API Google Ads <https://developers.google.com/google-ads/api/docs/start>, Yandex Direct <https://yandex.ru/dev/direct/>, Yandex Metrica <https://yandex.ru/dev/metrika/>, MyTarget <https://target.my.com/help/advertisers/api_arrangement/ru>, Vkontakte <https://vk.com/dev/methods>, Facebook <https://developers.facebook.com/docs/marketing-apis/> and AppsFlyer <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>. This packages allows you loading data from ads account and manage your ads materials.

This package provides a framework to assist creation of marine ecosystem models, generating either R or C++ code which can then be optimised using the TMB package and standard R tools. Principally designed to reproduce gadget2 models in TMB', but can be extended beyond gadget2's capabilities. Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) <doi:10.18637/jss.v070.i05> "TMB: Automatic Differentiation and Laplace Approximation.". Begley, J., & Howell, D. (2004) <https://core.ac.uk/download/pdf/225936648.pdf> "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.".

This package provides a statistical ecosystem modelling package, taking many features of the ecosystem into account. Gadget works by running an internal model based on many parameters, and then comparing the data from the output of this model to real data to get a goodness-of-fit likelihood score. These parameters can then be adjusted, and the model re-run, until an optimum is found, which corresponds to the model with the lowest likelihood score. Gadget allows the user to include a number of features into an ecosystem model: One or more species, each of which may be split into multiple stocks; multiple areas with migration between areas; predation between and within species; maturation; reproduction and recruitment; multiple commercial and survey fleets taking catches from the populations. For more details see <https://gadget-framework.github.io/gadget2/>. This is the C++ Gadget2 runtime, making it available for R.

Data from PMID 11102521.

This package performs Gamma regression, where both mean and shape parameters follows lineal regression structures.

Fits gastric emptying time series from MRI or scintigraphic measurements using nonlinear mixed-model population fits with nlme and Bayesian methods with Stan; computes derived parameters such as t50 and AUC.

An event-Based framework for building Shiny apps. Instead of relying on standard Shiny reactive objects, this package allow to relying on a lighter set of triggers, so that reactive contexts can be invalidated with more control.

This package provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.

This package provides functions and data are provided that support a course that emphasizes statistical issues of inference and generalizability. The functions are designed to make it straightforward to illustrate the use of cross-validation, the training/test approach, simulation, and model-based estimates of accuracy. Methods considered are Generalized Additive Modeling, Linear and Quadratic Discriminant Analysis, Tree-based methods, and Random Forests.

This package provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.

Estimation, forecasting, and simulation of generalized autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013) <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>. Model specification allows for various data types and distributions, different parametrizations, exogenous variables, joint and separate modeling of exogenous variables and dynamics, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method.

This is a supportive data package for the software package gage. However, the data supplied here are also useful for gene set or pathway analysis or microarray data analysis in general. In this package, we provide two demo microarray dataset: GSE16873 (a breast cancer dataset from GEO) and BMP6 (originally published as an demo dataset for GAGE, also registered as GSE13604 in GEO). This package also includes commonly used gene set data based on KEGG pathways and GO terms for major research species, including human, mouse, rat and budding yeast. Mapping data between common gene IDs for budding yeast are also included.

GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8).