This package provides methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

This package provides functions to compute the potential model as defined by Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available to customize the model, such as the possibility to fine-tune the distance friction functions or to use custom distance matrices. Some computations are parallelized to improve their efficiency.

This package provides functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting; <doi:10.1101/554725> , where methods are explained in detail.

Figures rendered on graphics devices are usually rescaled to fit pre-determined device dimensions. plotscale implements the reverse: desired plot dimensions are specified and device dimensions are calculated to accommodate marginal material, giving consistent proportions for plot elements. Default methods support grid graphics such as lattice and ggplot. See "example('devsize')" and "vignette('plotscale')".

Calculate parametric mortality and Fertility models, following packages BaSTA in Colchero, Jones and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and BaFTA <https://github.com/fercol/BaFTA>, summary statistics (e.g. ageing rates, life expectancy, lifespan equality, etc.), life table and product limit estimators from census data.

This package provides functions to access survey results directly into R using the Qualtrics API. Qualtrics <https://www.qualtrics.com/about/> is an online survey and data collection software platform. See <https://api.qualtrics.com/> for more information about the Qualtrics API. This package is community-maintained and is not officially supported by Qualtrics'.

Collection of functions to evaluate presence-absence models. It comprises functions to adjust discrimination statistics for the representativeness effect through case-weighting, along with functions for visualizing the outcomes. Originally outlined in: Jiménez-Valverde (2022) The uniform AUC: dealing with the representativeness effect in presence-absence models. Methods Ecol. Evol, 13, 1224-1236.

This package allows the estimation of hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956). Functions are also given to test via randomisations the significance of each F and variance components, using the likelihood-ratio statistics G.

fcitx5-rime provides the Rime input method engine for fcitx5. Rime is a lightweight, extensible input method engine supporting various input schemas including glyph-based input methods, romanization-based input methods as well as those for Chinese dialects. It has the ability to compose phrases and sentences intelligently and provide very accurate traditional Chinese output.

Variance Stabilized Transformation of Read Counts derived from Bgee RNA-Seq Expression Data. Expression Data includes annotations and is across 6 species (Homo sapiens, Mus musculus, Rattus norvegicus, Danio rerio, Drosophila melanogaster, and Caenorhabditis elegans) and across more than 132 tissues. The data is represented as a RData files and is available in ExperimentHub.

The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) <http://www.jstor.org/stable/3532933> has been used to estimate the bivariate time series data using Bayesian technique.

Uses data from the EPSG Registry to look up suitable coordinate reference system transformations for spatial datasets in R. Returns a data frame with CRS codes that can be used for CRS transformation and mapping projects. Please see the EPSG Dataset Terms of Use at <https://epsg.org/terms-of-use.html> for more information.

This package implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) <doi:10.1038/s41598-024-54149-y>.

Offers statistical methods to compare diagnostic performance between two binary diagnostic tests on the same subject in clinical studies. Includes functions for generating formatted tables to display diagnostic outcomes, facilitating a clear and comprehensive comparison directly through the R console. Inspired by and extending the functionalities of the DTComPair', tableone', and gtsummary packages.

This package provides a fast Rcpp'-based implementation of polynomially-computable voting theory methods for committee ranking and scoring. The package includes methods such as Approval Voting (AV), Satisfaction Approval Voting (SAV), sequential Proportional Approval Voting (PAV), and sequential Phragmen's Rule. Weighted variants of these methods are also provided, allowing for differential voter influence.

It calculates the alpha-quantile proposed by Daouia and Simar (2007) <doi:10.1016/j.jeconom.2006.07.002> and order-m efficiency score in multi-dimension proposed by Daouia and Gijbels (2011) <doi:10.1016/j.jeconom.2010.12.002> and computes several summaries and representation of the associated frontiers in 2d and 3d.

Convert data to GeoJSON or TopoJSON from various R classes, including vectors, lists, data frames, shape files, and spatial classes. geojsonio does not aim to replace packages like sp', rgdal', rgeos', but rather aims to be a high level client to simplify conversions of data from and to GeoJSON and TopoJSON'.

Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearsonâ s coefficient of skewness, Bowleyâ s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) <doi: 10.1007/s41060-018-0106-1>.

The time series forecasting framework for use with the tidymodels ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the forecast and prophet packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

This package provides an htmlwidgets <https://www.htmlwidgets.org/> interface to NGL.js <http://nglviewer.org/ngl/api/>. NGLvieweR can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications. It includes a set of API functions to manipulate the viewer after creation in Shiny.

Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data based on data nuggets. Reference papers: [1] Beavers et al., (2024) <doi:10.1080/10618600.2024.2341896>. [2] Duan, Y., Cabrera, J., & Emir, B. (2023). "A New Projection Pursuit Index for Big Data." <doi:10.48550/arXiv.2312.06465>.

Plots survival models from the survival package. Additionally, it plots curves of multistate models from the mstate package. Typically, a plot is drawn by the sequence survplot(), confIntArea(), survCurve() and nrAtRisk(). The separation of the plot in this 4 functions allows for great flexibility to make a custom plot for publication.

It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.

Generic methods for use in a time series probabilistic framework, allowing for a common calling convention across packages. Additional methods for time series prediction ensembles and probabilistic plotting of predictions is included. A more detailed description is available at <https://www.nopredict.com/packages/tsmethods> which shows the currently implemented methods in the tsmodels framework.