This package performs genetic algorithm (Scrucca, L (2013) <doi:10.18637/jss.v053.i04>) assisted genomic best liner unbiased prediction for genomic selection. It also provides a binning method in natural population for genomic selection under the principle of linkage disequilibrium for dimensional reduction.

This package creates tables suitable for regulatory agency submission by leveraging the gtsummary package as the back end. Tables can be exported to HTML, Word, PDF and more. Highly customized outputs are available by utilizing existing styling functions from gtsummary as well as custom options designed for regulatory tables.

Analyze small-sample clustered or longitudinal data with binary outcome using modified generalized estimating equations (GEE) with bias-adjusted covariance estimator. The package provides any combination of three GEE methods and 12 covariance estimators.

Fetch Professional Golfers Association (PGA) Tour tournament data from ESPN <https://www.espn.com/golf/> including leaderboards and hole-by-hole scoring. Data is returned in tidy tibble format ready for analysis. Supports local storage via RDS or Apache Arrow Parquet files for fast repeated access. Designed for golf analytics, data journalism, and fantasy sports research.

The function gggap() streamlines the creation of segments on the y-axis of ggplot2 plots which is otherwise not a trivial task to accomplish.

This function converts mfpr, numeric, or character strings representing numbers to bigq format without loss of precision.

The geographic dimension plays a fundamental role in multidimensional systems. To define a geographic dimension in a star schema, we need a table with attributes corresponding to the levels of the dimension. Additionally, we will also need one or more geographic layers to represent the data using this dimension. The goal of this package is to support the definition of geographic dimensions from layers of geographic information related to each other. It makes it easy to define relationships between layers and obtain the necessary data from them.

Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.

This package provides functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others. Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.

Computes the sample probability value (p-value) for the estimated coefficient from a standard genome-wide univariate regression. It computes the exact finite-sample p-value under the assumption that the measured phenotype (the dependent variable in the regression) has a known Bernoulli-normal mixture distribution. Finite-sample genome-wide regression p-values (Gwrpv) with a non-normally distributed phenotype (Gregory Connor and Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).

Fits Weighted Quantile Sum (WQS) regression (Carrico et al. (2014) <doi:10.1007/s13253-014-0180-3>), a random subset implementation of WQS (Curtin et al. (2019) <doi:10.1080/03610918.2019.1577971>), a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) and a WQS with 2 indices (Renzetti et al. (2023) <doi:10.3389/fpubh.2023.1289579>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.

This package provides ggplot2'-based visualization functions for output objects from the exametrika package, which implements test data engineering methods described in Shojima (2022, ISBN:978-981-16-9547-1). Supports a wide range of psychometric models including Item Response Theory, Latent Class Analysis, Latent Rank Analysis, Biclustering (binary, ordinal, and nominal), Bayesian Network Models, and related network models. All plot functions return ggplot2 objects that can be further customized by the user.

Bindings to the GeographicLib C++ library <https://geographiclib.sourceforge.io/> for precise geodetic calculations including geodesic computations (distance, bearing, paths, intersections), map projections (UTM/UPS, Transverse Mercator, Lambert Conformal Conic, and more), grid reference systems (MGRS, Geohash, GARS, Georef), coordinate conversions (geocentric, local Cartesian), and polygon area on the WGS84 ellipsoid. All functions are fully vectorized.

Algebra of operations for blending, copying, adjusting, and compositing layers in ggplot2'. Supports copying and adjusting the aesthetics or parameters of an existing layer, partitioning a layer into multiple pieces for re-composition, applying affine transformations to layers, and combining layers (or partitions of layers) using blend modes (including commutative blend modes, like multiply and darken). Blend mode support is particularly useful for creating plots with overlapping groups where the layer drawing order does not change the output; see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.

Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian two Poisson samples model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) <doi:10.1214/11-BJPS139>, Hamza & Vallois (2016) <doi:10.1016/j.spl.2016.03.014>, Chen & Novick (1984) <doi:10.3102/10769986009002163>.

The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method. By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed. This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others. First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user. By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions. The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem.

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.

Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.

This package provides functions to assess the goodness of fit of binary, multinomial and ordinal logistic models. Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests (ordinal).

Robust regression via gamma-divergence with L1, elastic net and ridge.

Sankey and alluvial diagrams visualise flows of quantities across stages in stacked bars. This package makes it easy to create such diagrams using ggplot2'.

An extension of ggplot2 to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.

This package provides ggplot2 extensions for creating skewed boxplots using several statistical methods (Kimber, 1990 <doi:10.2307/2347808>; Hubert and Vandervieren, 2008 <doi:10.1016/j.csda.2007.11.008>; Adil et al., 2015 <doi:10.18187/pjsor.v11i1.500>; Babura et al., 2017 <doi:10.1063/1.4982872>; Walker et al., 2018 <doi:10.1080/00031305.2018.1448891>). The package implements custom statistical transformations and geometries to visualize data distributions with an emphasis on skewness.

Calculates Agresti's generalized odds ratios. For a randomly selected pair of observations from two groups, calculates the odds that the second group will have a higher scoring outcome than that of the first group. Package provides hypothesis testing for if this odds ratio is significantly different to 1 (equal chance).