This package provides functions for rendering Bezier curves (Pomax, 2018) <https://pomax.github.io/bezierinfo/> in grid'. There is support for both quadratic and cubic Bezier curves. There are also functions for calculating points on curves, tangents to curves, and normals to curves.

William S. Cleveland's book Visualizing Data is a classic piece of literature on Exploratory Data Analysis. Although it was written several decades ago, its content is still relevant as it proposes several tools which are useful to discover patterns and relationships among the data under study, and also to assess the goodness of fit o a model. This package provides functions to produce the ggplot2 versions of the visualization tools described in this book and is thought to be used in the context of courses on Exploratory Data Analysis.

This package provides tools to streamline the extraction, processing, and visualization of Computable General Equilibrium (CGE) results from GTAP models. Designed for compatibility with both .har and .sl4 files, the package enables users to automate data preparation, apply mapping metadata, and generate high-quality plots and summary tables with minimal coding. GTAPViz supports flexible export options (e.g., Text, CSV, Stata', or Excel formats). This facilitates efficient post-simulation analysis for economic research and policy reporting. Includes helper functions to filter, format, and customize outputs with reproducible styling.

Read, analyze, modify, and write GAMS (General Algebraic Modeling System) data. The main focus of gamstransfer is the highly efficient transfer of data with GAMS <https://www.gams.com/>, while keeping these operations as simple as possible for the user. The transfer of data usually takes place via an intermediate GDX (GAMS Data Exchange) file. Additionally, gamstransfer provides utility functions to get an overview of GAMS data and to check its validity.

This package provides a simple wrapper for Wikipedia data. Specifically, this package looks to fill a gap in retrieving text data in a tidy format that can be used for Natural Language Processing.

This package provides a statistical hypothesis test for conditional independence. Given residuals from a sufficiently powerful regression, it tests whether the covariance of the residuals is vanishing. It can be applied to both discretely-observed functional data and multivariate data. Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas Peters (2022) <doi:10.1111/rssb.12544>.

Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL <https://oops.uni-oldenburg.de/id/eprint/4290>').

It is well known that the distribution of a Gaussian ratio does not follow a Gaussian distribution. The lack of awareness among users of vegetation indices about this non-Gaussian nature could lead to incorrect statistical modeling and interpretation. This package provides tools to accurately handle and analyse such ratios: density function, parameter estimation, simulation. An example on the study of chlorophyll fluorescence can be found in A. El Ghaziri et al. (2023) <doi:10.3390/rs15020528> and another method for parameter estimation is given in Bouhlel et al. (2023) <doi:10.23919/EUSIPCO58844.2023.10290111>.

An RStudio addin for teaching and learning making plot using the ggplot2 package. You can learn each steps of making plot by clicking your mouse without coding. You can get resultant code for the plot.

This package contains the Global Charcoal database data. Data include charcoal series (age, depth, charcoal quantity, associated units and methods) and information on sedimentary sites (localisation, depositional environment, biome, etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.

An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.

Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.

This package provides functions for performing polygon geometry with grid grobs. This allows complex shapes to be defined by combining simpler shapes.

Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>; van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.

Implementations of the algorithms present article Generalized Spatial-Time Sequence Miner, original title (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD - Simpósio Brasileiro de Banco de Dados, 2021 <doi:10.5753/sbbd.2021.17891>).

To provide a comprehensive analysis of high dimensional longitudinal data,this package provides analysis for any combination of 1) simultaneous variable selection and estimation, 2) mean regression or quantile regression for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced or imbalanced data, 5) moderate, high or even ultra-high dimensional data, via computationally efficient implementations of penalized generalized estimating equations.

Define, simulate, and validate stock-flow consistent (SFC) macroeconomic models. The godley R package offers tools to dynamically define model structures by adding variables and specifying governing systems of equations. With it, users can analyze how different macroeconomic structures affect key variables, perform parameter sensitivity analyses, introduce policy shocks, and visualize resulting economic scenarios. The accounting structure of SFC models follows the approach outlined in the seminal study by Godley and Lavoie (2007, ISBN:978-1-137-08599-3), ensuring a comprehensive integration of all economic flows and stocks. The algorithms implemented to solve the models are based on methodologies from Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205>, Peressini and Sullivan (1988, ISBN:0-387-96614-5), and contributions by Joao Macalos.

This package provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).

Collect marketing data from Google Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

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>.

Companion package for the manual guide-R : Guide pour lâ analyse de données dâ enquêtes avec R available at <https://larmarange.github.io/guide-R/>. guideR implements miscellaneous functions introduced in guide-R to facilitate statistical analysis and manipulation of survey data.

This package provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.

Supplies a set of functions to interface with bikeshare data following the General Bikeshare Feed Specification, allowing users to query and accumulate tidy datasets for specified cities/bikeshare programs.

Simulating composite endpoints with recurrent and terminal events under staggered entry, and for constructing one- and two-sample group sequential test statistics and monitoring boundaries based on the mean frequency function. Details will be available in an upcoming publication.