Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.

This package contains fast functions to calculate the exact Bayes posterior for the Sparse Normal Sequence Model, implementing the algorithms described in Van Erven and Szabo (2021, <doi:10.1214/20-BA1227>). For general hierarchical priors, sample sizes up to 10,000 are feasible within half an hour on a standard laptop. For beta-binomial spike-and-slab priors, a faster algorithm is provided, which can handle sample sizes of 100,000 in half an hour. In the implementation, special care has been taken to assure numerical stability of the methods even for such large sample sizes.

Import, process, summarize and visualize raw data from metabolic carts. See Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for more details on data processing.

Univariate time series forecasting with STL decomposition based Extreme Learning Machine hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

Create scaled ggplot representations of playing surfaces. Playing surfaces are drawn pursuant to rule-book specifications. This package should be used as a baseline plot for displaying any type of tracking data.

It is a framework to fit semiparametric regression estimators for the total parameter of a finite population when the interest variable is asymmetric distributed. The main references for this package are Sarndal C.E., Swensson B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, "Model Assisted Survey Sampling." Springer-Verlag) Cardozo C.A, Paula G.A. and Vanegas L.H. (2022) "Generalized log-gamma additive partial linear mdoels with P-spline smoothing", Statistical Papers. Cardozo C.A and Alonso-Malaver C.E. (2022). "Semi-parametric model assisted estimation in finite populations." In preparation.

Estimate average treatment effects (ATEs) in stratified randomized experiments. sreg supports a wide range of stratification designs, including matched pairs, n-tuple designs, and larger strata with many units â possibly of unequal size across strata. sreg is designed to accommodate scenarios with multiple treatments and cluster-level treatment assignments, and accommodates optimal linear covariate adjustment based on baseline observable characteristics. sreg computes estimators and standard errors based on Bugni, Canay, Shaikh (2018) <doi:10.1080/01621459.2017.1375934>; Bugni, Canay, Shaikh, Tabord-Meehan (2024+) <doi:10.48550/arXiv.2204.08356>; Jiang, Linton, Tang, Zhang (2023+) <doi:10.48550/arXiv.2201.13004>; Bai, Jiang, Romano, Shaikh, and Zhang (2024) <doi:10.1016/j.jeconom.2024.105740>; Bai (2022) <doi:10.1257/aer.20201856>; Bai, Romano, and Shaikh (2022) <doi:10.1080/01621459.2021.1883437>; Liu (2024+) <doi:10.48550/arXiv.2301.09016>; and Cytrynbaum (2024) <doi:10.3982/QE2475>.

Algorithms to create prognostic biomarkers using biological genesets or networks.

Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating the squared deviations from sample variance. Besides indicating the contribution of squared deviations for the sample variability, these plots are capable of detecting characteristics of the distribution such as symmetry, skewness and outliers. A remarkable graphical method based on Sv-plot2 can determine the decision on testing hypotheses over one or two population means. In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.

Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.

The saemix package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

This package provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.

This package provides a major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().

This package implements the Stratigraphic Plug Alignment (SPA) procedure for integrating sparsely sampled plug-based measurements (e.g., total organic carbon, porosity, mineralogy) with high-resolution X-ray fluorescence (XRF) geochemical data. SPA uses linear interpolation via the base approx() function with constrained extrapolation (rule = 1) to preserve stratigraphic order and avoid estimation beyond observed depths. The method aligns all datasets to a common depth grid, enabling high-resolution multivariate analysis and stratigraphic interpretation of core-based datasets such as those from the Utica and Point Pleasant formations. See R Core Team (2025) <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/stats-package.html> and Omodolor (2025) <http://rave.ohiolink.edu/etdc/view?acc_num=case175262671767524> for methodological background and geological context.

Plots that illustrate the flow of information or material.

Density, distribution function, quantile function and random generation for the sum of independent non-identical binomial distribution with parameters \codesize and \codeprob.

This package creates static / animated / interactive visualisations embeddable in R Markdown documents. It implements an R-to-JavaScript transpiler and enables users to write JavaScript applications using the syntax of R.

This package provides a simple authentification mechanism for single shiny applications. Authentification and password change functionality are performed calling user provided functions that typically access some database backend. Source code of main applications is protected until authentication is successful.

Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et al. (2016) <DOI:10.1111/rssb.12174>).

This package implements stochastic metafrontier analysis for productivity and performance benchmarking across firms operating under different technologies. Contains routines for the deterministic metafrontier envelope of O'Donnell et al. (2008) <doi:10.1007/s00181-007-0119-4> via linear and quadratic programming, and the stochastic metafrontier of Huang et al. (2014) <doi:10.1007/s11123-014-0402-2>. Also supports latent class stochastic metafrontier analysis and sample selection correction stochastic metafrontier models. Depends on the sfaR package by Dakpo et al. (2023) <https://CRAN.R-project.org/package=sfaR>.

Tidy snapshot of every episode of the public-television travel series Rick Steves Europe (2000-2025), enriched with IMDB ratings, geocoded destinations, ISO country codes, episode thumbnails, and descriptive summaries. Designed as a companion dataset for introductory data analysis and visualization in the spirit of the moderndive textbook: every row is an episode, every column is a candidate for a plot or a join. Compiled from public sources for teaching purposes; not an official or verified Rick Steves Europe dataset, and shared with the permission of the Rick Steves Europe team.

This package provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of lm': it contains methods such as summary(), plot(), confint() and predict(). The slm package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.

This package provides a collection of sample datasets on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data. It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R.

The goal of siteymlgen is to make it easy to organise the building of your R Markdown website. The init() function placed within the first code chunk of the index.Rmd file of an R project directory will initiate the generation of an automatically written _site.yml file. siteymlgen recommends a specific naming convention for your R Markdown files. This naming will ensure that your navbar layout is ordered according to a hierarchy.