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r-globe
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public |
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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2024-01-16 |
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r-glogis
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public |
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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2024-01-16 |
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r-globals
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public |
Identifies global ("unknown" or "free") objects in R expressions by code inspection using various strategies (ordered, liberal, or conservative). The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in parallel, distributed compute environments.
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2024-01-16 |
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r-globaloptions
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public |
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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2024-01-16 |
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r-glmx
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public |
Extended techniques for generalized linear models (GLMs), especially for binary responses, including parametric links and heteroscedastic latent variables.
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2024-01-16 |
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r-glmulti
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public |
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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2024-01-16 |
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r-glmertree
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public |
Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 'partykit'. The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>).
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2024-01-16 |
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r-gjrm
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public |
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
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2024-01-16 |
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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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2024-01-16 |
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r-glmnetutils
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public |
Provides a formula interface for the 'glmnet' package for elasticnet regression, a method for cross-validating the alpha parameter, and other quality-of-life tools.
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2024-01-16 |
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r-glmnetcr
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Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.
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2024-01-16 |
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r-glmmrr
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Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.
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2024-01-16 |
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r-glmc
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public |
Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.
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2024-01-16 |
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r-glmmadaptive
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Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) <doi:10.1080/10618600.1995.10474663>.
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2024-01-16 |
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r-githubinstall
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public |
Provides an helpful way to install packages hosted on GitHub.
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2024-01-16 |
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r-glmbb
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public |
Find all hierarchical models of specified generalized linear model with information criterion (AIC, BIC, or AICc) within specified cutoff of minimum value. Alternatively, find all such graphical models. Use branch and bound algorithm so we do not have to fit all models.
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2024-01-16 |
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r-glm2
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public |
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
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2024-01-16 |
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r-glm.predict
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public |
Functions to calculate predicted values and the difference between the two cases with confidence interval for lm() [linear model], glm() [generalised linear model], glm.nb() [negative binomial model], polr() [ordinal logistic model], multinom() [multinomial model] and tobit() [tobit model], svyglm() [survey-weighted generalised linear models], lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
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2024-01-16 |
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r-glba
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public |
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
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2024-01-16 |
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r-gldrm
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Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) <doi:10.1080/01621459.2013.824892>. Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>. Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
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2024-01-16 |
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r-glassdoor
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public |
Interacts with the 'Glassdoor' API <https://www.glassdoor.com/developer/index.htm>. Allows the user to search job statistics, employer statistics, and job progression, where 'Glassdoor' provides a breakdown of other jobs a person did after their current one.
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2024-01-16 |
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r-glarma
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Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
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2024-01-16 |
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r-giscor
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Tools to download data from the GISCO (Geographic Information System of the Commission) Eurostat database <https://ec.europa.eu/eurostat/web/gisco>. Global and European map data available. This package is in no way officially related to or endorsed by Eurostat.
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2024-01-16 |
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r-gk2011
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Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
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2024-01-16 |
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r-gimme
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Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.
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2024-01-16 |
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r-gitlink
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public |
Provides helpers to add 'Git' links to 'shiny' applications, 'rmarkdown' documents, and other 'HTML' based resources. This is most commonly used for 'GitHub' ribbons.
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2024-01-16 |
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r-ghql
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public |
A 'GraphQL' client, with an R6 interface for initializing a connection to a 'GraphQL' instance, and methods for constructing queries, including fragments and parameterized queries. Queries are checked with the 'libgraphqlparser' C++ parser via the 'gaphql' package.
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2024-01-16 |
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r-gitcreds
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Query, set, delete credentials from the 'git' credential store. Manage 'GitHub' tokens and other 'git' credentials. This package is to be used by other packages that need to authenticate to 'GitHub' and/or other 'git' repositories.
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2024-01-16 |
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r-git2rdata
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public |
The git2rdata package is an R package for writing and reading dataframes as plain text files. A metadata file stores important information. 1) Storing metadata allows to maintain the classes of variables. By default, git2rdata optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available in vignette("plain_text", package = "git2rdata"). 2) Storing metadata also allows smaller row based diffs between two consecutive commits. This is a useful feature when storing data as plain text files under version control. Details on this part of the implementation are available in vignette("version_control", package = "git2rdata"). Although we envisioned git2rdata with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. 3) git2rdata is a useful tool in a reproducible and traceable workflow. vignette("workflow", package = "git2rdata") gives a toy example. 4) vignette("efficiency", package = "git2rdata") provides some insight into the efficiency of file storage, git repository size and speed for writing and reading.
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2024-01-16 |
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r-gistr
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Work with 'GitHub' 'gists' from 'R' (e.g., <https://en.wikipedia.org/wiki/GitHub#Gist>, <https://docs.github.com/en/github/writing-on-github/creating-gists/>). A 'gist' is simply one or more files with code/text/images/etc. This package allows the user to create new 'gists', update 'gists' with new files, rename files, delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists', open a 'gist' in your default browser, get embed code for a 'gist', list 'gist' 'commits', and get rate limit information when 'authenticated'. Some requests require authentication and some do not. 'Gists' website: <https://gist.github.com/>.
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2024-01-16 |
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r-gipfrm
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public |
Maximum likelihood estimation under relational models, with or without the overall effect.
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2024-01-16 |
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r-giniwegneg
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Gini-based coefficients and plot of the ordinary and generalized curve of maximum inequality in the presence of weighted and negative attributes.
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2024-01-16 |
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r-gim
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public |
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.
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2024-01-16 |
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r-gillespiessa
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Provides a simple to use, intuitive, and extensible interface to several stochastic simulation algorithms for generating simulated trajectories of finite population continuous-time model. Currently it implements Gillespie's exact stochastic simulation algorithm (Direct method) and several approximate methods (Explicit tau-leap, Binomial tau-leap, and Optimized tau-leap). The package also contains a library of template models that can be run as demo models and can easily be customized and extended. Currently the following models are included, 'Decaying-Dimerization' reaction set, linear chain system, logistic growth model, 'Lotka' predator-prey model, Rosenzweig-MacArthur predator-prey model, 'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model. Pineda-Krch et al. (2008) <doi:10.18637/jss.v025.i12>.
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2024-01-16 |
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r-ggvenndiagram
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Easy-to-use functions to generate 2-7 sets Venn plot in publication quality. 'ggVennDiagram' plot Venn using well-defined geometry dataset and 'ggplot2'. The shapes of 2-4 sets Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which are developed and imported from another package 'venn', authored by Adrian Dusa. We provided internal functions to integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections of them, then separately plot Venn in three components: set edges, set labels, and regions. From version 1.0, it is possible to customize these components as you demand in ordinary 'ggplot2' grammar.
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2024-01-16 |
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r-ggthemes
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Some extra themes, geoms, and scales for 'ggplot2'. Provides 'ggplot2' themes and scales that replicate the look of plots by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata', 'Excel', and 'The Wall Street Journal', among others. Provides 'geoms' for Tufte's box plot and range frame.
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2024-01-16 |
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r-gifti
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Functions to read in the geometry format under the 'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 'GIFTI' <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data.
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2024-01-16 |
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r-ghs
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Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.
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2024-01-16 |
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r-ghibli
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Colour palettes inspired by Studio Ghibli <https://en.wikipedia.org/wiki/Studio_Ghibli> films, ported to R for your enjoyment.
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2024-01-16 |
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r-ggvis
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An implementation of an interactive grammar of graphics, taking the best parts of 'ggplot2', combining them with the reactive framework of 'shiny' and drawing web graphics using 'vega'.
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2024-01-16 |
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r-gh
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Minimal client to access the 'GitHub' 'API'.
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2024-01-16 |
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r-ggvenn
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An easy-to-use way to draw pretty venn diagram by 'ggplot2'.
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2024-01-16 |
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r-ggversa
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A collection of datasets for the upcoming book "Graficas versatiles con ggplot: Analisis visuales de datos", by Raymond L. Tremblay and Julian Hernandez-Serano.
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2024-01-16 |
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r-ggupset
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Replace the standard x-axis in 'ggplots' with a combination matrix to visualize complex set overlaps. 'UpSet' has introduced a new way to visualize the overlap of sets as an alternative to Venn diagrams. This package provides a simple way to produce such plots using 'ggplot2'. In addition it can convert any categorical axis into a combination matrix axis.
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2024-01-16 |
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r-ggthemeassist
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Rstudio add-in that delivers a graphical interface for editing 'ggplot2' theme elements.
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2024-01-16 |
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r-ggtern
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Extends the functionality of 'ggplot2', providing the capability to plot ternary diagrams for (subset of) the 'ggplot2' geometries. Additionally, 'ggtern' has implemented several NEW geometries which are unavailable to the standard 'ggplot2' release. For further examples and documentation, please proceed to the 'ggtern' website.
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2024-01-16 |
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r-ggsurvfit
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Ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper 'ggplot2' geoms or stat methods, allowing the functions from this package to be combined with any function or customization from 'ggplot2' and other 'ggplot2' extension packages.
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2024-01-16 |
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r-ggstream
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Make smoothed stacked area charts in 'ggplot2'. Stream plots are useful to show magnitude trends over time.
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2024-01-16 |
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r-ggtext
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A 'ggplot2' extension that enables the rendering of complex formatted plot labels (titles, subtitles, facet labels, axis labels, etc.). Text boxes with automatic word wrap are also supported.
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2024-01-16 |
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r-ggstatsplot
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Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.
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2024-01-16 |
