This package provides a collection of functions to perform Gaussian quadrature with different weight functions corresponding to the orthogonal polynomials in package orthopolynom. Examples verify the orthogonality and inner products of the polynomials.

An excerpt of the data available at Gapminder.org. For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007.

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>.

This is a dataset package for GANPA, which implements a network-based gene weighting approach to pathway analysis. This package includes data useful for GANPA, such as a functional association network, pathways, an expression dataset and multi-subunit proteins.

This is an add on package to GAMLSS. The purpose of this package is to allow users to defined truncated distributions in GAMLSS models. The main function gen.trun() generates truncated version of an existing GAMLSS family distribution.

Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.

Implementation of a common set of punctual solutions for Cooperative Game Theory.

An R interface to the Galvanize Highbond API <https://docs-apis.highbond.com>.

Interface for extra smooth functions including tensor products, neural networks and decision trees.

Allows user to choose/gate a region on the plot and returns points within it.

Display a random fact about Carl Friedrich Gauss based the on collection curated by Mike Cavers via the <http://gaussfacts.com> site.

This package provides functions to fit two-dimensional Gaussian functions, predict values from fits, and produce plots of predicted data via either ggplot2 or base R plotting.

Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.

This package provides functions to estimate the disparities across categories (e.g. Black and white) that persists if a treatment variable (e.g. college) is equalized. Makes estimates by treatment modeling, outcome modeling, and doubly-robust augmented inverse probability weighting estimation, with standard errors calculated by a nonparametric bootstrap. Cross-fitting is supported. Survey weights are supported for point estimation but not for standard error estimation; those applying this package with complex survey samples should consult the data distributor to select an appropriate approach for standard error construction, which may involve calling the functions repeatedly for many sets of replicate weights provided by the data distributor. The methods in this package are described in Lundberg (2021) <doi:10.31235/osf.io/gx4y3>.

This is an add-on package to gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.

Demos for smoothing and gamlss.family distributions.

This package provides data used as examples to demonstrate GAMLSS models.

This package contains infrastructure for using mboost::gamboost() in order to estimate multistate models.

Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.

This is an add-on package to GAMLSS. The purpose of this package is to allow users to fit interval response variables in GAMLSS models. The main function gen.cens() generates a censored version of an existing GAMLSS family distribution.

Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in Martinez-de-Pison (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.

The gasanalyzer R package offers methods for importing, preprocessing, and analyzing data related to photosynthetic characteristics (gas exchange, chlorophyll fluorescence and isotope ratios). It translates variable names into a standard format, and can recalculate derived, physiological quantities using imported or predefined equations. The package also allows users to assess the sensitivity of their results to different assumptions used in the calculations. See also Tholen (2024) <doi:10.1093/aobpla/plae035>.

This package provides a set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a log or a logit transformation, respectively.

Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) <doi:10.1007/s10182-023-00490-y>.