One can find single-stage and two-stage designs for a phase II single-arm study with either efficacy or safety/toxicity endpoints as described in Kim and Wong (2019) <doi:10.29220/CSAM.2019.26.2.163>.

Allows calculation on, and sampling from Gibbs Random Fields, and more precisely general homogeneous Potts model. The primary tool is the exact computation of the intractable normalising constant for small rectangular lattices. Beside the latter function, it contains method that give exact sample from the likelihood for small enough rectangular lattices or approximate sample from the likelihood using MCMC samplers for large lattices.

This package implements a geographically weighted non-negative principal components analysis, which consists of the fusion of geographically weighted and sparse non-negative principal components analyses <doi:10.17608/k6.auckland.9850826.v1>.

The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.

Triangular and trapezoidal fuzzy numbers are used to study fuzzy logic, fuzzy reasoning and approximating, fuzzy regression models, etc. This package builds the generating function for triangular and trapezoidal fuzzy numbers based on Souliotis et al. (2022)<doi:10.3390/math10183350>. They proposed a method for the construction of fuzzy numbers via a cumulative distribution function based on the possibility theory.

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.

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>.

Create geographically referenced traffic data from the Google Maps JavaScript API <https://developers.google.com/maps/documentation/javascript/examples/layer-traffic>.

This package provides an easy to use interface to the Google Pub/Sub REST API <https://cloud.google.com/pubsub/docs/reference/rest>.

Create interactive visualization charts to draw data in three dimensional graphs. The graphs can be included in Shiny apps and R markdown documents, or viewed from the R console and RStudio Viewer. Based on the vis.js Graph3d module and the htmlwidgets R package.

Introduces a Copilot'-like completion experience, but it knows how to talk to the objects in your R environment. ellmer chats are integrated directly into your RStudio and Positron sessions, automatically incorporating relevant context from surrounding lines of code and your global environment (like data frame columns and types). Open the package dialog box with a keyboard shortcut, type your request, and the assistant will stream its response directly into your documents.

Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) <doi:10.1080/01621459.2013.824892>. Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>. Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.

This package implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) <doi:10.1007/s11042-020-10139-6> and M. Clerc (2012) <https://hal.archives-ouvertes.fr/hal-00764996>.

This package contains the development of a tool that provides a web-based graphical user interface (GUI) to perform Techniques from a subset of spatial statistics known as geographically weighted (GW) models. Contains methods described by Brunsdon et al., 1996 <doi:10.1111/j.1538-4632.1996.tb00936.x>, Brunsdon et al., 2002 <doi:10.1016/s0198-9715(01)00009-6>, Harris et al., 2011 <doi:10.1080/13658816.2011.554838>, Brunsdon et al., 2007 <doi:10.1111/j.1538-4632.2007.00709.x>.

This package provides complete detailed preprocessing of two-dimensional gas chromatogram (GCxGC) samples. Baseline correction, smoothing, peak detection, and peak alignment. Also provided are some analysis functions, such as finding extracted ion chromatograms, finding mass spectral data, targeted analysis, and nontargeted analysis with either the National Institute of Standards and Technology Mass Spectral Library or with the mass data. There are also several visualization methods provided for each step of the preprocessing and analysis.

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>.

Create epicurves, epigantt charts, and diverging bar charts using ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation (time series data). Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the ggplot2', (D) labelling of the last value of a time-series. An introduction on how to use epicurves can be found on the US CDC website (2012, <https://www.cdc.gov/training/quicklearns/epimode/index.html>).

This package provides tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries.

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <doi:10.48550/arXiv.1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <doi:10.48550/arXiv.1706.02205> and MaxMin ordering proposed in Guinness (2018) <doi:10.48550/arXiv.1609.05372>.

Functionality for adding the geological timescale to bivariate plots.

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.

This package provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.

Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts, compositions, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. The methods mainly follow Tolosana-Delgado, Mueller and van den Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.

The git2rdata package is an R package for writing and reading dataframes as plain text files. A metadata file stores important information. 1) Storing metadata allows to maintain the classes of variables. By default, git2rdata optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available in vignette("plain_text", package = "git2rdata"). 2) Storing metadata also allows smaller row based diffs between two consecutive commits. This is a useful feature when storing data as plain text files under version control. Details on this part of the implementation are available in vignette("version_control", package = "git2rdata"). Although we envisioned git2rdata with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. 3) git2rdata is a useful tool in a reproducible and traceable workflow. vignette("workflow", package = "git2rdata") gives a toy example. 4) vignette("efficiency", package = "git2rdata") provides some insight into the efficiency of file storage, git repository size and speed for writing and reading.