This package provides the Jester Dataset for package recommenderlab.

Accurate prediction of subject recruitment for Randomized Clinical Trials (RCT) remains an ongoing challenge. Many previous prediction models rely on parametric assumptions. We present functions for non-parametric RCT recruitment prediction under several scenarios.

This package provides a dataset of functions in all base and recommended packages of R versions 0.50 onwards.

This package provides a method for fitting the entire regularization path of the reluctant generalized additive model (RGAM) for linear regression, logistic, Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019) <arXiv:1912.01808> for details.

Estimation of abundance and other demographic parameters for closed populations, open populations and the robust design in capture-recapture experiments using loglinear models.

Estimates robust rank-based fixed effects and predicts robust random effects in two- and three- level random effects nested models. The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.

This package provides an R interface to the JuliaBUGS.jl package (<https://github.com/TuringLang/JuliaBUGS.jl>) for Bayesian inference using the BUGS modeling language. Allows R users to run models in Julia and return results as familiar R objects. Visualization and posterior analysis are supported via the bayesplot and posterior packages.

This package provides the log-likelihoods with gradients from stan (Carpenter et al (2015), <doi:10.48550/arXiv.1509.07164>) needed for generalized log-likelihood estimation in nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is split of to reduce computational burden of recompiling rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs the nlmixr2 models during estimation.

This package provides methods for ranking responses of a single response question or a multiple response question are described in the two papers: 1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of Applied Statistics, 35, 465-474. <DOI:10.1080/02664760801924533> 2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple Response Questions. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177, 191-208. <DOI:10.1111/rssa.12009>.

It contains Chinese character frequency data based on news data from 2017 to 2019. Source of these news include Sina, China daily and Tencent.

Estimating repeatability (intra-class correlation) from Gaussian, binary, proportion and Poisson data.

The routine twosample_test() in this package runs the two sample test using various test statistic. The p values are found via permutation or large sample theory. The routine twosample_power() allows the calculation of the power in various cases, and plot_power() draws the corresponding power graphs. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones.

Collection of functions for fitting distributions to given data or by known quantiles. Two main functions fit.perc() and fit.cont() provide users a GUI that allows to choose a most appropriate distribution without any knowledge of the R syntax. Note, this package is a part of the rrisk project.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The rdpower package provides tools to perform power, sample size and MDE calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.

This package provides a tool for processing Articulate Assistant Advancedâ ¢ (AAA) ultrasound tongue imaging data and Carstens AG500/1 electro-magnetic articulographic data.

Algorithms to price American and European equity options, convertible bonds and a variety of other financial derivatives. It uses an extension of the usual Black-Scholes model in which jump to default may occur at a probability specified by a power-law link between stock price and hazard rate as found in the paper by Takahashi, Kobayashi, and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>. We use ideas and techniques from Andersen and Buffum (2002) <doi:10.2139/ssrn.355308> and Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.

Exchange rate for Kenya Shilling against other currencies, US DOLLAR, EURO, STERLING POUND, Tanzania Shilling, Uganda Shilling.

Easily interact with the Arduino Iot Cloud API <https://www.arduino.cc/reference/en/iot/api/>, managing devices, things, properties and data.

This package provides a robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance.

This package provides a Minimal Example Package which demonstrates mlpack use via C++ Code from R.

To incorporate neighbor genotypic identity into genome-wide association studies, the package provides a set of functions for variation partitioning and association mapping. The theoretical background of the method is described in Sato et al. (2021) <doi:10.1038/s41437-020-00401-w>.

Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) <doi:10.48550/arXiv.2403.11954>. For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2024) <doi:10.48550/arXiv.2407.18835> with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases.

Apply sensitivity analysis for offline policy evaluation, as implemented in Jung et al. (2017) <arXiv:1702.04690> based on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.

R packages for genetics research.