This package provides data about the Star Wars movie franchise in a set of relational tables or as a complete DuckDB database. All data was collected from the open source Star Wars API.

This package provides functions for simplified emulation of time series computer model output in model parameter space using Gaussian processes. Stilt can be used more generally for Kriging of spatio-temporal fields. There are functions to predict at new parameter settings, to test the emulator using cross-validation (which includes information on 95% confidence interval empirical coverage), and to produce contour plots over 2D slices in model parameter space.

Statistical models for specific coronavirus disease 2019 use cases at German local health authorities. All models of Statistical modelling for infectious disease management smidm are part of the decision support toolkit in the EsteR project. More information is published in Sonja Jäckle, Rieke Alpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max Westphal "'EsteR â A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities" (2022) <doi:10.3233/SHTI220799> and Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions" (2021) <doi:10.3390/ijerph18179166>.

Read SubRip <https://sourceforge.net/projects/subrip/> subtitle files as data frames for easy text analysis or manipulation. Easily shift numeric timings and export subtitles back into valid SubRip timestamp format to sync subtitles and audio.

Unofficial client for Sentry <https://sentry.io>, a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the Plumber package.

This package provides functions to read and write ESRI shapefiles.

Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption.

In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.

This package implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.

This package implements different kinds of bootstraps to estimate sampling variation from survey data with complex designs. Includes the rescaled bootstrap described in Rust and Rao (1996) <doi:10.1177/096228029600500305> and Rao and Wu (1988) <doi:10.1080/01621459.1988.10478591>.

The goal of stim is to provide a function for estimating the Stability Informed Model. The Stability Informed Model integrates stability information (how much a variable correlates with itself in the future) into cross-sectional estimates. Wysocki and Rhemtulla (2022) <https://psyarxiv.com/vg5as>.

Data from statistical agencies and other institutions are mostly confidential. This package, introduced in Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>, can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>. Various risk estimation and anonymization methods are included. Note that the package includes a graphical user interface published in Meindl and Templ (2019) <doi:10.3390/a12090191> that allows to use various methods of this package.

This package implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardà a M, López-Vizcaà no E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.

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

Pull data from the STAT Search Analytics API <https://help.getstat.com/knowledgebase/api-services/>. It was developed by the Search Discovery team to help analyze keyword ranking data.

Fast, lightweight toolkit for data splitting. Data sets can be partitioned into disjoint groups (e.g. into training, validation, and test) or into (repeated) k-folds for subsequent cross-validation. Besides basic splits, the package supports stratified, grouped as well as blocked splitting. Furthermore, cross-validation folds for time series data can be created. See e.g. Hastie et al. (2001) <doi:10.1007/978-0-387-84858-7> for the basic background on data partitioning and cross-validation.

The cartogram heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on "The states most threatened by trade" (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). "State bins" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.

Uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.

Easily use Blueprint', the popular React library from Palantir, in your Shiny app. Blueprint provides a rich set of UI components for creating visually appealing applications and is optimized for building complex, data-dense web interfaces. This package provides most components from the underlying library, as well as special wrappers for some components to make it easy to use them in R without writing JavaScript code.

Extract the signed backbones of intrinsically dense weighted networks based on the significance filter and vigor filter as described in the following paper. Please cite it if you find this software useful in your work. Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. <arXiv:2012.05216>.

Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.635>; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) <doi:10.2436/20.8080.02.121>; A. Iparragirre, I. Barrio (2024) <doi:10.1007/978-3-031-65723-8_7>.

The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of Sim.PLFN package are: (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that Sim.PLFN package works on the basis of FuzzyNumbers package.

This package provides functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps. Lower-level functions map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.

This package provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) <doi:10.1007/s13253-023-00550-5>.