You can use this function to easily draw a combined histogram and restricted cubic spline. The function draws the graph through ggplot2'. RCS fitting requires the use of the rcs() function of the rms package. Can fit cox regression, logistic regression. This method was described by Per Kragh (2003) <doi:10.1002/sim.1497>.

Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).

Create what we call Elemental Graphics for display of anova results. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular anova methods. This package represents a modification of the original granova package; the key change is to use ggplot2', Hadley Wickham's package based on Grammar of Graphics concepts (due to Wilkinson). The main function is granovagg.1w() (a graphic for one way ANOVA); two other functions (granovagg.ds() and granovagg.contr()) are to construct graphics for dependent sample analyses and contrast-based analyses respectively. (The function granova.2w(), which entails dynamic displays of data, is not currently part of granovaGG'.) The granovaGG functions are to display data for any number of groups, regardless of their sizes (however, very large data sets or numbers of groups can be problematic). For granovagg.1w() a specialized approach is used to construct data-based contrast vectors for which anova data are displayed. The result is that the graphics use a straight line to facilitate clear interpretations while being faithful to the standard effect test in anova. The graphic results are complementary to standard summary tables; indeed, numerical summary statistics are provided as side effects of the graphic constructions. granovagg.ds() and granovagg.contr() provide graphic displays and numerical outputs for a dependent sample and contrast-based analyses. The graphics based on these functions can be especially helpful for learning how the respective methods work to answer the basic question(s) that drive the analyses. This means they can be particularly helpful for students and non-statistician analysts. But these methods can be of assistance for work-a-day applications of many kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. In the case of granovagg.1w() and granovagg.ds() several arguments are provided to facilitate flexibility in the construction of graphics that accommodate diverse features of data, according to their corresponding display requirements. See the help files for individual functions.

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.

This package provides a Gibbs sampler corresponding to a Group Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates. Hyperparameters in the GIGG prior specification can either be fixed by the user or can be estimated via Marginal Maximum Likelihood Estimation. Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar Mukherjee (2021) <arXiv:2102.10670>.

The main purpose of this package is to allow fitting of mixture distributions with generalised additive models for location scale and shape models see Chapter 7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.

Convert data to GeoJSON or TopoJSON from various R classes, including vectors, lists, data frames, shape files, and spatial classes. geojsonio does not aim to replace packages like sp', rgdal', rgeos', but rather aims to be a high level client to simplify conversions of data from and to GeoJSON and TopoJSON'.

This package provides tools for efficient processing of large, whole genome genotype data sets in variant call format (VCF). It includes several functions to calculate commonly used population genomic metrics and a method for reference panel free genotype imputation, which is described in the preprint Gurke & Mayer (2024) <doi:10.22541/au.172515591.10119928/v1>.

This package contains the Gene ontology terms and skeleton for the reduced GO directed acyclic graph (DAG) for the organisms Rat and Mouse. The methods are explicitly discussed in the following article : Manjang et al (2020) <doi:10.1038/s41598-020-73326-3>.

Extra geoms and scales for ggplot2', including geom_cloud(), a Normal density cloud replacement for errorbars; transforms ssqrt_trans and pseudolog10_trans, which are loglike but appropriate for negative data; interp_trans() and warp_trans() which provide scale transforms based on interpolation; and an infix compose operator for scale transforms.

Flexible and robust estimation and inference of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2019) <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 <doi:10.32614/RJ-2021-057> for an overview of the package.

Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. It comes with features to accept data formats in plink(.raw) and VCF. It provides users flexibility to extract and filter individuals, mutations and genes of interest.

Group Sequential Operating Characteristics for Clinical, Bayesian two-arm Trials with known Sigma and Normal Endpoints, as described in Gerber and Gsponer (2016) <doi: 10.18637/jss.v069.i11>.

Integrating applied psychological and psychometric methods into geographical analysis. With the emergence of geo-referenced questionnaires, spatially explicit psychological and psychometric methods can offer a geographically contextualised approach that reflects latent traits and processes at a more local scale, leading to more tailored research and decision-making processes. The implemented methods include Geographically Weighted Cronbach's alpha and its bandwidth selection. See Zhang & Li (2025) <doi:10.1111/gean.70021>.

Estimation and inference using the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) framework, a flexible method for solving ill-posed inverse problems and parameter estimation under uncertainty (Golan, Judge, and Miller (1996, ISBN:978-0471145925) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) <doi:10.1007/978-3-031-97589-9_21>). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) <doi:10.1007/978-3-030-26036-1_2> "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) <doi:10.1080/03610918.2022.2057540>) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints.

We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>. The recent procedures presented in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019) <doi:10.1007/s00184-019-00708-7> are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) <doi:10.1080/00949650108812100>.

This package implements the generalized order-restricted information criterion approximation (GORICA), an AIC-like information criterion that can be utilized to evaluate informative hypotheses specifying directional relationships between model parameters in terms of (in)equality constraints (see Altinisik, Van Lissa, Hoijtink, Oldehinkel, & Kuiper, 2021), <doi:10.31234/osf.io/t3c8g>. The GORICA is applicable not only to normal linear models, but also to generalized linear models (GLMs), generalized linear mixed models (GLMMs), structural equation models (SEMs), and contingency tables. For contingency tables, restrictions on cell probabilities can be non-linear.

This package provides helpers to add Git links to shiny applications, rmarkdown documents, and other HTML based resources. This is most commonly used for GitHub ribbons.

This package provides a set of geometries to make line plots a little bit nicer. Use along with ggplot2 to: - Improve the clarity of line plots with many overlapping lines - Draw more realistic worms.

Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.

This package provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.

Download and process public domain works in the Project Gutenberg collection <https://www.gutenberg.org/>. Includes metadata for all Project Gutenberg works, so that they can be searched and retrieved.

Método simples e eficiente de geolocalizar dados no Brasil. O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatà sticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatà stica (IBGE), órgão oficial de estatà sticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatà sticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatà stica (IBGE), Brazil's official statistics and geography agency.).

Integrates with your RMarkdown documents to automatically publish figures to the <https://GoFigr.io> service. Supports both knitr and interactive execution within RStudio'.