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r-gnorm
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Functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.
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2025-04-22 |
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r-gmt
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Interface between the GMT map-making software and R, enabling the user to manipulate geographic data within R and call GMT commands to draw and annotate maps in postscript format. The gmt package is about interactive data analysis, rapidly visualizing subsets and summaries of geographic data, while performing statistical analysis in the R console.
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2025-04-22 |
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r-gmodels
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Various R programming tools for model fitting.
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2025-04-22 |
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r-gmmboost
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Likelihood-based Boosting for Generalized mixed models
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2025-04-22 |
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r-gmm
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It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
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2025-04-22 |
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r-gmfd
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Some methods for the inference and clustering of univariate and multivariate functional data, using a generalization of Mahalanobis distance, along with some functions useful for the analysis of functional data. For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.
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2025-04-22 |
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r-gmediation
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Current version of this R package conducts mediation path analysis for multiple mediators in two stages.
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2025-04-22 |
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r-gmdhreg
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Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko. Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons. An introduction of GMDH algorithms: Farlow, S.J. (1981): "The GMDH algorithm of Ivakhnenko", The American Statistician, 35(4), pp. 210-215. <doi:10.2307/2683292> Ivakhnenko A.G. (1968): "The Group Method of Data Handling - A Rival of the Method of Stochastic Approximation", Soviet Automatic Control, 13(3), pp. 43-55.
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2025-04-22 |
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r-gmdh
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Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.
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2025-04-22 |
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r-gmdatabase
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A template for a geometallurgical database and a fast and easy interface for accessing it is provided in this package.
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2025-04-22 |
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r-gmapsdistance
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Get distance and travel time between two points from Google Maps. Four possible modes of transportation (bicycling, walking, driving and public transportation).
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2025-04-22 |
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r-gmac
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Performs genomic mediation analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale mediation analysis and adaptively selects potential confounding variables to adjust for each mediation test from a pool of candidate confounders. The package is tailored for but not limited to genomic mediation analysis (e.g., cis-gene mediating trans-gene regulation pattern where an eQTL, its cis-linking gene transcript, and its trans-gene transcript play the roles as treatment, mediator and the outcome, respectively), restricting to scenarios with the presence of cis-association (i.e., treatment-mediator association) and random eQTL (i.e., treatment).
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2025-04-22 |
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r-glsme
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Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
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2025-04-22 |
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r-glrth
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Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.
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2025-04-22 |
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r-glrt
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Functions to conduct four generalized logrank tests and a score test under a proportional hazards model
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2025-04-22 |
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r-glogis
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Tools for the generalized logistic distribution (Type I, also known as skew-logistic distribution), encompassing basic distribution functions (p, q, d, r, score), maximum likelihood estimation, and structural change methods.
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2025-04-22 |
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r-globe
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Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.
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2025-04-22 |
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r-globals
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Identifies global ("unknown" or "free") objects in R expressions by code inspection using various strategies, e.g. conservative or liberal. The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in distributed compute environments.
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2025-04-22 |
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r-globaloptions
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It provides more configurations on the option values such as validation and filtering on the values, making options invisible or private.
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2025-04-22 |
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r-globalgsa
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Implementation of three different Gene set analysis (GSA) algorithms for combining the individual pvalues of a set of genetic variats (SNPs) in a gene level pvalue. The implementation includes the selection of the best inheritance model for each SNP.
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2025-04-22 |
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r-glmx
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Extended techniques for generalized linear models (GLMs), especially for binary responses, including parametric links and heteroskedastic latent variables.
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2025-04-22 |
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r-glmulti
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Automated model selection and model-averaging. Provides a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.
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2025-04-22 |
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r-glmtlp
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It provides an extremely efficient procedure for fitting the entire truncated lasso regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. The algorithm uses the difference of convex technique. The detail of the algorithm is described in Shen, Pan and Zhu (2012) <doi:10.1080/01621459.2011.645783>. The package is inherited from a popular R package 'glmnet' and many functions in 'glmnet' can be directly used in 'glmtlp'. You can learn more details by the online manual (<http://wuchong.org/glmtlp.html>).
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2025-04-22 |
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r-glmsdata
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Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.
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2025-04-22 |
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r-glm.predict
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Functions to calculate predicted values and the difference between the two cases with confidence interval for lm() [linear model], glm() [general linear model], glm.nb() [negative binomial model], polr() [ordinal logistic model] and multinom() [multinomial model] using Monte Carlo simulations. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
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2025-04-22 |
