This package provides functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.

This package implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.

Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.

The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.

Numerical integration with Gram polynomials (based on <arXiv:2106.14875> [math.NA] 28 Jun 2021, by Irfan Muhammad [School of Computer Science, University of Birmingham, UK]).

Adds a tintshade aesthetic to ggplot2 that varies the lightness of a mapped colour or fill within each hue group, keeping the plotted layer and its legend in sync. Supports both nested designs (each item belongs to one hue) and crossed designs (each lightness level appears across hues).

Statistical functions to fit, validate and describe a Generalized Waring Regression Model (GWRM).

This package provides probability density functions and sampling algorithms for three key distributions from the General Unimodal Distribution (GUD) family: the Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t distribution, and the Two-Piece Scale (TPSC) Student-t distribution. Additionally, this package includes a function for Bayesian linear modal regression, leveraging these three distributions for model fitting. The details of the Bayesian modal regression model based on the GUD family can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.

Gaussian-means (G-means) clustering is a clustering algorithm that extends the k-means algorithm by automatically determining the number of clusters.

Fits Generalised Linear Models (GLMs) with sparse and dense Matrix matrices for memory efficiency. Acts as a wrapper for the glm4() function in the MatrixModels package <doi:10.32614/CRAN.package.MatrixModels>, but adds convenient model methods and functions designed to mimic those associated with the glm() function from the stats package.

Informal implementation of some algorithms from Graph Theory and Combinatorial Optimization which arise in the subject "Graphs and Network Optimization" from first course of the EUPLA degree of Data Engineering in Industrial Processes.

Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs', using Google TensorFlow'. greta lets you write your own model like in BUGS', JAGS and Stan', except that you write models right in R, it scales well to massive datasets, and itâ s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum. This work is discussed at Golding (2019) <doi:10.21105/joss.01601>.

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.

This package implements the five-parameter Generalized Kumaraswamy ('gkw') distribution proposed by Carrasco, Ferrari and Cordeiro (2010) <doi:10.48550/arXiv.1004.0911> and its seven nested sub-families for modeling bounded continuous data on the unit interval (0,1). The gkw distribution extends the Kumaraswamy distribution described by Jones (2009) <doi:10.1016/j.stamet.2008.04.001>. Provides density, distribution, quantile, and random generation functions, along with analytical log-likelihood, gradient, and Hessian functions implemented in C++ via RcppArmadillo for maximum computational efficiency. Suitable for modeling proportions, rates, percentages, and indices exhibiting complex features such as asymmetry, or heavy tails and other shapes not adequately captured by standard distributions like simple Beta or Kumaraswamy.

Generalized Turnbull's estimator proposed by Dehghan and Duchesne (2011).

Calculates the cost of crossing in terms of the number of individuals and generations, which is theoretically formulated by Servin et al. (2004) <DOI:10.1534/genetics.103.023358>. This package has been designed for selecting appropriate parental genotypes and find the most efficient crossing scheme for gene pyramiding, especially for plant breeding.

Preview what a ggplot2 plot would look like if you save it to a file. Attach picture dimensions as a canvas() element and get an instant preview. These dimensions will then be used when you save the plot.

This package provides tools to adjust estimates of learning for guessing-related bias in educational and survey research. Implements standard guessing correction methods and latent class models that leverage informative pre-post test transitions to account for guessing behavior. The package helps researchers obtain more accurate estimates of actual learning when respondents may guess on closed-ended knowledge items. For theoretical background and empirical validation, see Cor and Sood (2016) <https://gsood.com/research/papers/guess.pdf>.

This package provides a new take on the bar chart. Similar to a waffle style chart but instead of squares the layout resembles a brick wall.

Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <https://www.tesourodireto.com.br/>.

This package implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).

This package provides a collection of layers for ggplot2'. Provides geoms built on linear and radial gradients from the grid package, giving areas, bars, paths, rectangles, and ridgelines a fading or glowing visual effect. Also includes mathematically driven layers â catenary curves, Chaikin's corner-cutting smoothing (Chaikin, 1974, <doi:10.1016/0146-664X(74)90028-8>), and Fourier-series reconstruction â plus Lexis diagrams, isotype bar charts.

This package provides a ggplot2 based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using ggplot2 techniques.

This package provides statistical methods to check if a parametric family of conditional density functions fits to some given dataset of covariates and response variables. Different test statistics can be used to determine the goodness-of-fit of the assumed model, see Andrews (1997) <doi:10.2307/2171880>, Bierens & Wang (2012) <doi:10.1017/S0266466611000168>, Dikta & Scheer (2021) <doi:10.1007/978-3-030-73480-0> and Kremling & Dikta (2024) <doi:10.48550/arXiv.2409.20262>. As proposed in these papers, the corresponding p-values are approximated using a parametric bootstrap method.