This package implements general unilateral loading estimator for two-layer latent factor models with smooth, element-wise factor transformations. We provide data simulation, loading estimation,finite-sample error bounds, and diagnostic tools for zero-mean and sub-Gaussian assumptions. A unified interface is given for evaluating estimation accuracy and cosine similarity. The philosophy of the package is described in Guo G. (2026) <doi:10.1016/j.apm.2025.116280>.

We implement two main functions. The first function uses a given grouped and/or right-censored grouping scheme and empirical data to infer parameters, and implements chi-square goodness-of-fit tests. The second function searches for the global optimal grouping scheme of grouped and/or right-censored count responses in surveys.

This package provides an R interface to the GeoNetwork API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a GeoNetwork web-application and expose it to OGC CSW.

This package provides tools for geometric morphometric analyses and multidimensional data. Implements methods for morphological disparity analysis using bootstrap and rarefaction, as reviewed in Foote (1997) <doi:10.1146/annurev.ecolsys.28.1.129>. Includes integration and modularity testing, following Fruciano et al. (2013) <doi:10.1371/journal.pone.0069376>, using Escoufier's RV coefficient as test statistic as well as two-block partial least squares - PLS, Rohlf and Corti (2000) <doi:10.1080/106351500750049806>. Also includes vector angle comparisons, orthogonal projection for data correction (Burnaby (1966) <doi:10.2307/2528217>; Fruciano (2016) <doi:10.1007/s00427-016-0537-4>), and parallel analysis for dimensionality reduction (Buja and Eyuboglu (1992) <doi:10.1207/s15327906mbr2704_2>).

Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities.

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>.

The gRbase package provides graphical modelling features used by e.g. the packages gRain', gRim and gRc'. gRbase implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. gRbase facilitates array operations, gRbase implements functions for testing for conditional independence. gRbase illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>) and in the paper by Dethlefsen and Højsgaard, (2005, <doi:10.18637/jss.v014.i17>). Please see citation("gRbase") for citation details.

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>.

Palettes based on video games.

Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package is for learning purposes and allows users to optimize various functions or parameters by mimicking biological evolution processes such as selection, crossover, and mutation. Ideal for tasks like machine learning parameter tuning, mathematical function optimization, and solving an optimization problem that involves finding the best solution in a discrete space.

This package performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.

Allows get address and port of the free proxy server, from one of two services <http://gimmeproxy.com/> or <https://getproxylist.com/>. And it's easy to redirect your Internet connection through a proxy server.

Functionalities for modelling functional data with multidimensional inputs, multivariate functional data, and non-separable and/or non-stationary covariance structure of function-valued processes. In addition, there are functionalities for functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure depends on functional covariates. The development version of the package can be found on <https://github.com/gpfda/GPFDA-dev>.

This package provides functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.

Graphical tools and goodness-of-fit tests for right-censored data: 1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests, which use the empirical distribution function for complete data and are extended for right-censored data. 2. Generalized chi-squared-type test, which is based on the squared differences between observed and expected counts using random cells with right-censored data. 3. A series of graphical tools such as probability or cumulative hazard plots to guide the decision about the most suitable parametric model for the data. These functions share several features as they can handle both complete and right-censored data, and they provide parameter estimates for the distributions under study.

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/>.

Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.

This package provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators. H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arXiv:1906.05776>.

Maximum likelihood estimation, random values generation, density computation and other functions for the exponential-Poisson generalised exponential-Poisson and Poisson-exponential distributions. References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. <doi:10.1080/02664763.2016.1268571>. Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. <doi:10.1080/03610918.2018.1491988>. Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. <doi:10.1016/j.spl.2009.09.003>.

Bindings to the libgraphqlparser C++ library. Parses GraphQL <https://graphql.org> syntax and exports the AST in JSON format.

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.

This package implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi: 10.1080/10705511.2014.915380>. And, it can also implement AIPE on RMSEA and power analysis on RMSEA.

This package provides a unified framework for sparse-group regularization and precision matrix estimation in Gaussian graphical models. It implements multiple sparse-group penalties, including sparse-group lasso, sparse-group adaptive lasso, sparse-group SCAD, and sparse-group MCP, and solves them efficiently using ADMM-based optimization. The package is designed for high-dimensional network inference where both sparsity and group structure are present.

In gene-expression microarray studies, for example, one generally obtains a list of dozens or hundreds of genes that differ in expression between samples and then asks What does all of this mean biologically? Alternatively, gene lists can be derived conceptually in addition to experimentally. For instance, one might want to analyze a group of genes known as housekeeping genes. The work of the Gene Ontology (GO) Consortium <geneontology.org> provides a way to address that question. GO organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. The role of GoMiner is to automate the mapping between a list of genes and GO, and to provide a statistical summary of the results as well as a visualization.