Implement maximum likelihood estimation for Poisson generalized linear models with grouped and right-censored count data. Intended to be used for analyzing grouped and right-censored data, which is widely applied in many branches of social sciences. The algorithm implemented is described in Fu et al., (2021) <doi:10.1111/rssa.12678>.

This package provides adaptive association tests for SNP level, gene level and pathway level analyses.

This package provides a fast C++ implementation of the design-based, Diffusion Decision Model (DDM) and the Linear Ballistic Accumulation (LBA) model. It enables the user to optimise the choice response time model by connecting with the Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampler implemented in the ggdmc package. The package fuses the hierarchical modelling, Bayesian inference, choice response time models and factorial designs, allowing users to build their own design-based models. For more information on the underlying models, see the works by Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>, and Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.

This General Regression Neural Networks Package uses various distance functions. It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous published paper Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>. This package includes various functions, although "euclidean" distance is used traditionally.

The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes rule.

This package provides tools for working with polygons with holes in ggplot2', with a new geom for drawing a polypath applying the evenodd or winding rules.

This package provides plotting functions for visualizing pedigrees and family trees. The package complements a behavior genetics package BGmisc [Garrison et al. (2024) <doi:10.21105/joss.06203>] by rendering pedigrees using the ggplot2 framework. Features include support for duplicated individuals, complex mating structures, integration with simulated pedigrees, and layout customization. Due to the impending deprecation of kinship2, version 1.0 incorporates the layout helper functions from kinship2. The pedigree alignment algorithms are adapted from kinship2 [Sinnwell et al. (2014) <doi:10.1159/000363105>]. We gratefully acknowledge the original authors: Jason Sinnwell, Terry Therneau, Daniel Schaid, and Elizabeth Atkinson for their foundational work.

Create correlation heatmaps with ggplot2 and customise them with flexible annotation and clustering. Symmetric heatmaps can use triangular or mixed layouts, removing redundant information or displaying complementary information in the two halves. There is also support for general heatmaps not displaying correlations.

Visualise overlapping time series lines as a heatmap of line density. Provides a ggplot2 statistic implementing the DenseLines algorithm, which "normalizes time series by the arc length to compute accurate densities" (Moritz and Fisher, 2018) <doi:10.48550/arXiv.1808.06019>.

The geom_rain() function adds different geoms together using ggplot2 to create raincloud plots.

Homogenize GNSS (Global Navigation Satellite System) time-series. The general model is a segmentation in the mean model including a periodic function and considering monthly variances, see Quarello (2020) <arXiv:2005.04683>.

An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.

This package provides a framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.

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>.

It provides a custom ggplot2 geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. It is useful for visualizing data that spans multiple days and for highlighting diurnal patterns.

Designed to customize ggplot graphics according to the institutional identity of the University of Ljubljana.

Fits user-specified (GLM-) models with group lasso penalty.

This package performs test procedures for general hypothesis testing problems for four multivariate coefficients of variation (Ditzhaus and Smaga, 2023 <arXiv:2301.12009>). We can verify the global hypothesis about equality as well as the particular hypotheses defined by contrasts, e.g., we can conduct post hoc tests. We also provide the simultaneous confidence intervals for contrasts.

On Galaxy platforms like Galaxy Europe <https://usegalaxy.eu>, many tools and workflows can run directly on a high-performance computer. GalaxyR connects R with Galaxy platforms API <https://usegalaxy.eu/api/docs> and allows credential management, uploading data, invoking workflows or tools, checking their status, and downloading results.

Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.

This package contains functions to create life history parameter plots from raw data. The plots are created using ggplot2', and calculations done using the tidyverse collection of packages. The package contains references to FishBase (Froese R., Pauly D., 2023) <https://www.fishbase.se/>.

This package provides a simple and flexible tool designed to create enriched figures and tables by providing a way to add text around them through predefined or custom layouts. Any input which is convertible to grob is supported, like ggplot', gt or flextable'. Based on R grid graphics, for more details see Paul Murrell (2018) <doi:10.1201/9780429422768>.

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.

Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>. Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.