Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <doi:10.1007/bf02295641> and Dusseldorp, Conversano, Van Os (2010) <doi:10.1198/jcgs.2010.06089>, integrating a regression tree and a multiple regression model.

Tool for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with it. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results.

Time series area-level models for small area estimation. The package supplements the functionality of the sae package. Specifically, it includes EBLUP fitting of the Rao-Yu model in the original form without a spatial component. The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing the assumption of stationarity. Both univariate and multivariate applications are supported. Of particular note is the allowance for covariance of the area-level sample estimates over time, as encountered in rotating panel designs such as the U.S. National Crime Victimization Survey or present in a time-series of 5-year estimates from the American Community Survey. Key references to the methods include J.N.K. Rao and I. Molina (2015, ISBN:9781118735787), J.N.K. Rao and M. Yu (1994) <doi:10.2307/3315407>, and R.E. Fay and R.A. Herriot (1979) <doi:10.1080/01621459.1979.10482505>.

Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and Knudson (2018) <arXiv:1812:09384>.

Computes bounds and sensitivity parameters as part of sensitivity analysis for selection bias. Different bounds are provided: the SV (Smith and VanderWeele), sharp bounds, AF (assumption-free) bound, GAF (generalized AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity parameters for the SV, sharp, and GAF bounds assume an additional dependence structure in form of a generalized M-structure. The bounds can be calculated for any structure as long as the necessary assumptions hold. See Smith and VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>, Zetterstrom, Sjölander, and Waernabum (2025) <doi:10.1177/09622802251374168>, Zetterstrom and Waernbaum (2022) <doi:10.1515/em-2022-0108>, and Zetterstrom (2024) <doi:10.1515/em-2023-0033>.

Non-parametric test, originally proposed by Stute (1997) <https://www.jstor.org/stable/2242560>, that the expectation of a dependent variable Y given an independent variable D is linear in D.

Compute Time series Resistant Smooth 4253H, twice smoothing method.

Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.

Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].

Efficient estimation of multivariate skew-normal distribution in closed form.

R client and utilities for Seven Bridges Platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. API documentation is hosted publicly at <https://docs.sevenbridges.com/docs/the-api>.

Slurm', Simple Linux Utility for Resource Management <https://slurm.schedmd.com/>, is a popular Linux based software used to schedule jobs in HPC (High Performance Computing) clusters. This R package provides a specialized lightweight wrapper of Slurm with a syntax similar to that found in the parallel R package. The package also includes a method for creating socket cluster objects spanning multiple nodes that can be used with the parallel package.

This package provides functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.

This package creates stratum orthogonal arrays (also known as strong orthogonal arrays). These are arrays with more levels per column than the typical orthogonal array, and whose low order projections behave like orthogonal arrays, when collapsing levels to coarser strata. Details are described in Groemping (2022) "A unifying implementation of stratum (aka strong) orthogonal arrays" <http://www1.bht-berlin.de/FB_II/reports/Report-2022-002.pdf>.

This package provides tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).

Carries out a two-level sample selection where the possibility of an initially selected site not wanting to participate is anticipated, and the site is optimally replaced. The procedure aims to reduce bias (and/or loss of external validity) with respect to the target population. In selecting units and sub-units, sitepickR uses the cube method developed by Deville & Tillé', (2004) <http://www.math.helsinki.fi/msm/banocoss/Deville_Tille_2004.pdf> and described in Tillé (2011) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2011002/article/11609-eng.pdf?st=5-sx8Q8n>. The cube method is a probability sampling method that is designed to satisfy criteria for balance between the sample and the population. Recent research has shown that this method performs well in simulations for studies of educational programs (see Fay & Olsen (2021, under review). To implement the cube method, sitepickR uses the sampling R package <https://cran.r-project.org/package=sampling>. To implement statistical matching, sitepickR uses the MatchIt R package <https://cran.r-project.org/package=MatchIt>.

Load WARC (Web ARChive) files into Apache Spark using sparklyr'. This allows to read files from the Common Crawl project <http://commoncrawl.org/>.

Proposes a torch implementation of Graph Net architecture allowing different options for message passing and feature embedding.

An user-friendly framework to preprocess raw item scores of questionnaires into factors or scores and standardize them. Standardization can be made either by their normalization in representative sample, or by import of premade scoring table.

Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions.

This package provides a helper function, to bulk read SQL code from separate files and load it into an R list, where the list elements contain the individual statements and queries as strings. This works by annotating the SQL code with a name comment, which also will be the name of the list element.

This package provides a small set of helper functions to convert sjPlot HTML-tables to R data.frame objects / knitr::kable-tables.

Enables reading and writing binary and ASCII data to RS232/RS422/RS485 or any other virtual serial interface of the computer.

Develop outstanding shiny apps for iOS and Android as well as beautiful shiny gadgets. shinyMobile is built on top of the latest Framework7 template <https://framework7.io>. Discover 14 new input widgets (sliders, vertical sliders, stepper, grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) combined with the power of server-side notifications such as alerts, modals, toasts, action sheets, sheets (and more) as well as 3 layouts (single, tabs and split).