This package provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y>.

GTFS-realtime is a format transit agencies use to provide current vehicle positions, predicted arrival times, and service alerts. This package provides efficient functions to read this format into data frames. It can be used to retrieve current data or to process archived data.

This package provides a not-so-comprehensive list of methods for estimating graphon, a symmetric measurable function, from a single or multiple of observed networks. For a detailed introduction on graphon and popular estimation techniques, see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>. It also contains several auxiliary functions for generating sample networks using various network models and graphons.

Make it easy to create simplified trial summary (TS) domain based on FDA FDA guide <https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.

This is a wrapper for the command line tool googler', which can be found at the following URL: <https://github.com/jarun/googler>.

This package implements methods to plot periodic data in any arbitrary range on the fly.

Design and analysis of group sequential designs for negative binomial outcomes, as described by T Mütze, E Glimm, H Schmidli, T Friede (2018) <doi:10.1177/0962280218773115>.

Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains functions to calculate haplotype specific odds ratio and the power of two stage design for GWAS studies.

Parameter estimation and prediction of Gaussian Process Classifier models as described in Bachoc et al. (2020) <doi:10.1007/S10898-020-00920-0>. Important functions : gpcm(), predict.gpcm(), update.gpcm().

The vegan package includes several functions for adding features to ordination plots: ordiarrows(), ordiellipse(), ordihull(), ordispider() and ordisurf(). This package adds these same features to ordination plots made with ggplot2'. In addition, gg_ordibubble() sizes points relative to the value of an environmental variable.

The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean and the standard deviation of a quantitative variable. It also flags potential outliers (together with their frequencies) that are c standard deviations away from the mean.

Graph clustering using an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models. The method is described in the article "Model-based clustering of multiple networks with a hierarchical algorithm" by T. Rebafka (2022) <arXiv:2211.02314>.

Computational representations of glycan compositions and structures, including details such as linkages, anomers, and substituents. Supports varying levels of monosaccharide specificity (e.g., "Hex" or "Gal") and ambiguous linkages. Provides robust parsing and generation of IUPAC-condensed structure strings. Optimized for vectorized operations on glycan structures, with efficient handling of duplications. As the cornerstone of the glycoverse ecosystem, this package delivers the foundational data structures that power glycomics and glycoproteomics analysis workflows.

This package implements various Gifi methods in a user-friendly way: categorical principal component analysis (princals), multiple correspondence analysis (homals), monotone regression analysis (morals).

Make R scripts reproducible, by ensuring that every time a given script is run, the same version of the used packages are loaded (instead of whichever version the user running the script happens to have installed). This is achieved by using the command groundhog.library() instead of the base command library(), and including a date in the call. The date is used to call on the same version of the package every time (the most recent version available at that date). Load packages from CRAN, GitHub, or Gitlab.

Gaussian processes ('GPs') have been widely used to model spatial data, spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on GPs with spatial data, spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the Matérn family and the Confluent Hypergeometric family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating GPs with all the implemented covariance functions; (5) unified implementation to allow easy specification of various GPs'.

This package provides residual global fit indices for generalized latent variable models.

This package provides functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.

Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).

Computes the probability density, survival function, the hazard rate functions and generates random samples from the GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. The likelihood estimates, the randomized quantile (Louzada, F., et al. (2020) <doi:10.1109/ACCESS.2020.3040525>) residuals and the normally transformed randomized survival probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>) residuals are obtained for the GTDL model.

Fits Generalised Linear Models (GLMs) with sparse and dense Matrix matrices for memory efficiency. Acts as a wrapper for the glm4() function in the MatrixModels package <doi:10.32614/CRAN.package.MatrixModels>, but adds convenient model methods and functions designed to mimic those associated with the glm() function from the stats package.

Generating, evaluating, and selecting initialization strategies for Gaussian Mixture Models (GMMs), along with functions to run the Expectation-Maximization (EM) algorithm. Initialization methods are compared using log-likelihood, and the best-fitting model can be selected using BIC. Methods build on initialization strategies for finite mixture models described in Michael and Melnykov (2016) <doi:10.1007/s11634-016-0264-8> and Biernacki et al. (2003) <doi:10.1016/S0167-9473(02)00163-9>, and on the EM algorithm of Dempster et al. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>. Background on model-based clustering includes Fraley and Raftery (2002) <doi:10.1198/016214502760047131> and McLachlan and Peel (2000, ISBN:9780471006268).

This package provides tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries.

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.