Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979) <doi:10.1111/j.1469-8986.1979.tb02991.x> as described in Blume & Cajochen (2021) <doi:10.1016/j.mex.2021.101318>.

This package provides functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.

Edit SVG files created in Inkscape by replacing placeholders (e.g. a rectangle element or in a text box) by ggplot2 objects, images or text. This helps automate the creation of figures with complex layouts.

Add indicators (spinner, progress bar, gif) in your shiny applications to show the user that the server is busy. And other tools to let your users know something is happening (send notifications, reports, ...).

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>.

Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" <doi:10.48550/arXiv.2008.05693>.

Synthesize numeric, categorical, mixed and time series data. Data circumstances including mixed (or zero-inflated) distributions and missing data patterns are reproduced in the synthetic data. A single parameter allows balancing between high-quality synthetic data that represents correlations of the original data and lower quality but more privacy safe synthetic data without correlations. Tuning can be done per variable or for the whole dataset.

Track and record the use of applications and the user's interactions with Shiny inputs. Allows to trace the inputs with which the user interacts, the outputs generated, as well as the errors displayed in the interface.

In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.

This package provides functions and data sets inspired by data sharpening - data perturbation to achieve improved performance in nonparametric estimation, as described in Choi, E., Hall, P. and Rousson, V. (2000). Capabilities for enhanced local linear regression function and derivative estimation are included, as well as an asymptotically correct iterated data sharpening estimator for any degree of local polynomial regression estimation. A cross-validation-based bandwidth selector is included which, in concert with the iterated sharpener, will often provide superior performance, according to a median integrated squared error criterion. Sample data sets are provided to illustrate function usage.

Allows a Simile model saved as a compiled binary to be loaded, parameterized, executed and interrogated. This version works with Simile v6 on.

Simulate complex data from a given directed acyclic graph and information about each individual node. Root nodes are simply sampled from the specified distribution. Child Nodes are simulated according to one of many implemented regressions, such as logistic regression, linear regression, poisson regression or any other function. Also includes a comprehensive framework for discrete-time simulation, discrete-event simulation, and networks-based simulation which can generate even more complex longitudinal and dependent data. For more details, see Robin Denz, Nina Timmesfeld (2025) <doi:10.48550/arXiv.2506.01498>.

Calculates sample size for various scenarios, such as sample size to estimate population proportion with stated absolute or relative precision, testing a single proportion with a reference value, to estimate the population mean with stated absolute or relative precision, testing single mean with a reference value and sample size for comparing two unpaired or independent means, comparing two paired means, the sample size For case control studies, estimating the odds ratio with stated precision, testing the odds ratio with a reference value, estimating relative risk with stated precision, testing relative risk with a reference value, testing a correlation coefficient with a specified value, etc. <https://www.academia.edu/39511442/Adequacy_of_Sample_Size_in_Health_Studies#:~:text=Determining%20the%20sample%20size%20for,may%20yield%20statistically%20inconclusive%20results.>.

Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book.

This package provides flexible hazard ratio curves allowing non-linear relationships between continuous predictors and survival. To better understand the effects that each continuous covariate has on the outcome, results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived.

Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.

The developed package can be used to generate a spatial population for different levels of relationships among the dependent and auxiliary variables along with spatially varying model parameters. A spatial layout is designed as a [0,k-1]x[0,k-1] square region on which observations are collected at (k x k) lattice points with a unit distance between any two neighbouring points along the horizontal and vertical axes. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<doi:10.1080/10485252.2018.1499907>. The generated spatial population can be utilized in Geographically Weighted Regression model based analysis for studying the spatially varying relationships among the variables. Furthermore, various statistical analysis can be performed on this spatially generated data.

Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).

I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.

Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.

This package implements the SISAL algorithm by Tikka and Hollmén. It is a sequential backward selection algorithm which uses a linear model in a cross-validation setting. Starting from the full model, one variable at a time is removed based on the regression coefficients. From this set of models, a parsimonious (sparse) model is found by choosing the model with the smallest number of variables among those models where the validation error is smaller than a threshold. Also implements extensions which explore larger parts of the search space and/or use ridge regression instead of ordinary least squares.

This package provides a graphical user interface for cross-sectional network modeling with the statnet software suite <https://github.com/statnet>.

Calculates (unconditional) post-selection confidence intervals and p-values for the coefficients of (generalized) linear models.

Interfaces with the SigOpt API. More info at <https://sigopt.com>.