Enables user to perform the following: 1. Roll n number of die/dice (roll()). 2. Toss n number of coin(s) (toss()). 3. Play the game of Rock, Paper, Scissors. 4. Choose n number of card(s) from a pack of 52 playing cards (Joker optional).

This package implements tools for the analysis of partially ordered data, with a particular focus on the evaluation of multidimensional systems of indicators and on the analysis of poverty. References, Fattore M. (2016) <doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016) <doi:10.1007/s11205-016-1501-4> Arcagni A. (2017) <doi:10.1007/978-3-319-45421-4_19>.

Data are partitioned (clustered) into k clusters "around medoids", which is a more robust version of K-means implemented in the function pam() in the cluster package. The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>. Please refer to the pam() function documentation for more references. Clustered data is plotted as a split heatmap allowing visualisation of representative "group-clusters" (medoids) in the data as separated fractions of the graph while those "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.

This package provides tools for the design of prospective studies using Personalised Synthetic Controls. Can be used in either single arm or randomised studies.

Pivot easily by specifying rows, columns, values and split.

The Pearson-ICA algorithm is a mutual information-based method for blind separation of statistically independent source signals. It has been shown that the minimization of mutual information leads to iterative use of score functions, i.e. derivatives of log densities. The Pearson system allows adaptive modeling of score functions. The flexibility of the Pearson system makes it possible to model a wide range of source distributions including asymmetric distributions. The algorithm is designed especially for problems with asymmetric sources but it works for symmetric sources as well.

Investigate (analytically or visually) the inputs and outputs of probabilistic analyses of health economic models using standard health economic visualisation and metamodelling methods.

This package provides tools for the test for the comparison of survival curves, the evaluation of the goodness-of-fit and the predictive capacity of the proportional hazards model.

Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.

Includes functions for keyword search of pdf files. There is also a wrapper that includes searching of all files within a single directory.

Projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation.

Power logit regression models for bounded continuous data, in which the density generator may be normal, Student-t, power exponential, slash, hyperbolic, sinh-normal, or type II logistic. Diagnostic tools associated with the fitted model, such as the residuals, local influence measures, leverage measures, and goodness-of-fit statistics, are implemented. The estimation process follows the maximum likelihood approach and, currently, the package supports two types of estimators: the usual maximum likelihood estimator and the penalized maximum likelihood estimator. More details about power logit regression models are described in Queiroz and Ferrari (2022) <arXiv:2202.01697>.

Games that can be played in the R console. Includes coin flip, hangman, jumble, magic 8 ball, poker, rock paper scissors, shut the box, spelling bee, and 2048.

This package provides a standardized framework to support the selection and evaluation of parametric survival models for time-to-event data. Includes tools for visualizing survival data, checking proportional hazards assumptions (Grambsch and Therneau, 1994, <doi:10.1093/biomet/81.3.515>), comparing parametric (Ishak and colleagues, 2013, <doi:10.1007/s40273-013-0064-3>), spline (Royston and Parmar, 2002, <doi:10.1002/sim.1203>) and cure models, examining hazard functions, and evaluating model extrapolation. Methods are consistent with recommendations in the NICE Decision Support Unit Technical Support Documents (14 and 21 <https://sheffield.ac.uk/nice-dsu/tsds/survival-analysis>). Results are structured to facilitate integration into decision-analytic models, and reports can be generated with rmarkdown'. The package builds on existing tools including flexsurv (Jackson, 2016, <doi:10.18637/jss.v070.i08>)) and flexsurvcure for estimating cure models.

We included functions to assess the performance of risk models. The package contains functions for the various measures that are used in empirical studies, including univariate and multivariate odds ratios (OR) of the predictors, the c-statistic (or area under the receiver operating characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test, reclassification table, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Also included are functions to create plots, such as risk distributions, ROC curves, calibration plot, discrimination box plot and predictiveness curves. In addition to functions to assess the performance of risk models, the package includes functions to obtain weighted and unweighted risk scores as well as predicted risks using logistic regression analysis. These logistic regression functions are specifically written for models that include genetic variables, but they can also be applied to models that are based on non-genetic risk factors only. Finally, the package includes function to construct a simulated dataset with genotypes, genetic risks, and disease status for a hypothetical population, which is used for the evaluation of genetic risk models.

We provide comprehensive draft data for major professional sports leagues, including the National Football League (NFL), National Basketball Association (NBA), and National Hockey League (NHL). It offers access to both historical and current draft data, allowing for detailed analysis and research on player biases and player performance. The package is useful for sports fans and researchers interested in identifying biases and trends within scouting reports. Created by web scraping data from leading websites that cover professional sports player scouting reports, the package allows users to filter and summarize data for analytical purposes. For further details on the methods used, please refer to Wickham (2022) "rvest: Easily Harvest (Scrape) Web Pages" <https://CRAN.R-project.org/package=rvest> and Harrison (2023) "RSelenium: R Bindings for Selenium WebDriver" <https://CRAN.R-project.org/package=RSelenium>.

Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, Learning Curves, Design Structure Matrices, and more.

This package provides a collection of software provides R support for ADMB (Automatic Differentiation Model Builder) and a GUI interface facilitates the conversion of ADMB template code to C code followed by compilation to a binary executable. Stand-alone functions can also be run by users not interested in clicking a GUI'.

Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort).

Price comparisons within or between countries provide an overall measure of the relative difference in prices, often denoted as price levels. This package provides index number methods for such price comparisons (e.g., The World Bank, 2011, <doi:10.1596/978-0-8213-9728-2>). Moreover, it contains functions for sampling and characterizing price data.

Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple way to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <doi:10.48550/arXiv.2104.00721>.

This package provides functions to automatically build a directory structure for a new R project. Using this structure, ProjectTemplate automates data loading, preprocessing, library importing and unit testing.

This package provides functions which can be used to support the Multicriteria Decision Analysis (MCDA) process involving multiple criteria, by PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations).

Extends the Heckman selection framework to panel data with individual random effects. The first stage models participation via a panel Probit specification, while the second stage can take a panel linear, Probit, Poisson, or Poisson log-normal form. Model details are provided in Bailey and Peng (2025) <doi:10.2139/ssrn.5475626> and Peng and Van den Bulte (2024) <doi:10.1287/mnsc.2019.01897>.