This package provides a model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.

Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.

The program GRNN implements the algorithm proposed by Specht (1991).

This package implements regression models for bounded continuous data in the open interval (0,1) using the five-parameter Generalized Kumaraswamy distribution. Supports modeling all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of predictors through various link functions. Provides efficient maximum likelihood estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics, model comparison tools, and simulation methods. Particularly useful for analyzing proportions, rates, indices, and other bounded response data with complex distributional features not adequately captured by simpler models.

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

This package provides two functions that generate source code implementing the predict function of fitted glm objects. In this version, code can be generated for either C or Java'. The idea is to provide a tool for the easy and fast deployment of glm predictive models into production. The source code generated by this package implements two function/methods. One of such functions implements the equivalent to predict(type="response"), while the second implements predict(type="link"). Source code is written to disk as a .c or .java file in the specified path. In the case of c, an .h file is also generated.

Writes SAS code to get predicted values from every tree of a gbm.object.

Estimation of the effect of each income source on income inequalities based on the decomposition of Lerman and Yitzhaki (1985) <doi:10.2307/1928447>.

The aim of this package is to offer more variability of graphics based on the self-organizing maps.

This package provides a post-estimation method for categorical response models (CRM). Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm() are currently supported. Available tests include the Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal models. The proportional odds, adjacent-category, and constrained continuation-ratio models are particularly supported at ordinal level. Tests for the proportional odds assumptions in ordinal models are also possible with the Brant and the Likelihood-Ratio tests. Moreover, several summary measures of predictive strength (Pseudo R-squared), and some useful error metrics, including, the brier score, misclassification rate and logloss are also available for the binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) <http://www.statmod.org/workshops_archive_proceedings_2018.html>.

Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package ganGenerativeData <https://cran.r-project.org/package=ganGenerativeData>.

This package implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.

An interface to the Gmail RESTful API. Allows access to your Gmail messages, threads, drafts and labels.

This package implements a geographically weighted partial correlation which is an extension from gwss() function in the GWmodel package (Percival and Tsutsumida (2017) <doi:10.1553/giscience2017_01_s36>).

Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.

This package provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG). To learn how to use it, check the vignettes for a quick tutorial. Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300> together with those relevant references from the one listed below. The package is based on the research developed at the Chair of Systems Design, ETH Zurich. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <doi:10.48550/arXiv.1607.02441>. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>. Casiraghi, G., (2017) <doi:10.48550/arXiv.1702.02048>. Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>. Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>. Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>. Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.

Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.

Generate commonly used plots in the field of design of experiments using ggplot2'. ggDoE currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot, regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs, contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design.

This package provides tools for creating publication-ready dimensionality reduction plots, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). This package helps visualize high-dimensional data with options for custom labels, density plots, and faceting, using the ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

Four graph-based tests are provided for testing whether two samples are from the same distribution. It works for both continuous data and discrete data.

This package provides a collection of functions to set up Google Public Data Explorer <https://www.google.com/publicdata/> data visualization tool with your own data, building automatically the corresponding DataSet Publishing Language file, or DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to be published in Public Data Explorer'.

Facilitates the creation of page layout visualizations in which words are represented as rectangles with sizes relating to the length of the words. Which then is divided in lines and pages for easy overview of up to quite large texts.

Find all hierarchical models of specified generalized linear model with information criterion (AIC, BIC, or AICc) within specified cutoff of minimum value. Alternatively, find all such graphical models. Use branch and bound algorithm so we do not have to fit all models.

Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. The package enables rapid evaluation of likelihood functions for both single- and multi-subject models across trial-level data. It also offers fast initialisation of starting parameters for genetic sampling with many Markov chains, facilitating estimation in complex models typically found in experimental psychology and behavioural science. These optimisations help reduce computational overhead in large-scale model fitting tasks.