Adds smoothing spline modelling capability to nlme. Fits smoothing spline terms in Gaussian linear and nonlinear mixed-effects models.

This package provides tools for common operations on lists. Provided are short-cuts to operations like selecting and merging data stored in lists. The functions in this package are designed to be used with pipes.

The aim of the package is to create data objects which allow for accesses like x["test"] and x["test","test"].

Estimate and confidence/credible intervals for an unknown regressor x0 given an observed y0.

Computes power, or sample size or the detectable difference for a repeated measures model with attrition. It requires the variance covariance matrix of the observations but can compute this matrix for several common random effects models. See Diggle, P, Liang, KY and Zeger, SL (1994, ISBN:9780198522843).

Estimation of life expectancy and Life Years Lost (LYL, or lillies for short) for a given population, for example those with a given disease or condition. In addition, the package can be used to compare estimates from different populations, or to estimate confidence intervals. Technical details of the method are available in Plana-Ripoll et al. (2020) <doi:10.1371/journal.pone.0228073> and Andersen (2017) <doi:10.1002/sim.7357>.

Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted.

This package provides a tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper Anderson et al. (2015) <doi:10.1101/2022.03.24.485545>.

Agricultural data for 1888-2021 from the Morrow Plots at the University of Illinois. The world's second oldest ongoing agricultural experiment, the Morrow Plots measure the impact of crop rotation and fertility treatments on corn yields. The data includes planting information and annual yield measures for corn grown continuously and in rotation with other crops, in treated and untreated soil.

This package implements the three parallel forecast combinations of Markov Switching GARCH and extreme learning machine model along with the selection of appropriate model for volatility forecasting. For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>.

This package provides graph-constrained regression methods in which regularization parameters are selected automatically via estimation of equivalent Linear Mixed Model formulation. riPEER (ridgified Partially Empirical Eigenvectors for Regression) method employs a penalty term being a linear combination of graph-originated and ridge-originated penalty terms, whose two regularization parameters are ML estimators from corresponding Linear Mixed Model solution; a graph-originated penalty term allows imposing similarity between coefficients based on graph information given whereas additional ridge-originated penalty term facilitates parameters estimation: it reduces computational issues arising from singularity in a graph-originated penalty matrix and yields plausible results in situations when graph information is not informative. riPEERc (ridgified Partially Empirical Eigenvectors for Regression with constant) method utilizes addition of a diagonal matrix multiplied by a predefined (small) scalar to handle the non-invertibility of a graph Laplacian matrix. vrPEER (variable reducted PEER) method performs variable-reduction procedure to handle the non-invertibility of a graph Laplacian matrix.

This package provides access to teaching materials for various statistics courses, including R and Python programs, Shiny apps, data, and PDF/HTML documents. These materials are stored on the Internet as a ZIP file (e.g., in a GitHub repository) and can be downloaded and displayed or run locally. The content of the ZIP file is temporarily or permanently stored. By default, the package uses the GitHub repository sigbertklinke/mmstat4.data. Additionally, the package includes association_measures.R from the archived package ryouready by Mark Heckman and some auxiliary functions.

You can use the set of wrappers for analytical schemata to reduce the effort in writing machine-readable data. The set of all-in-one wrappers will cover widely used functions from data analysis packages.

Computing functional traits-based distances between pairs of species for species gathered in assemblages allowing to build several functional spaces. The package allows to compute functional diversity indices assessing the distribution of species (and of their dominance) in a given functional space for each assemblage and the overlap between assemblages in a given functional space, see: Chao et al. (2018) <doi:10.1002/ecm.1343>, Maire et al. (2015) <doi:10.1111/geb.12299>, Mouillot et al. (2013) <doi:10.1016/j.tree.2012.10.004>, Mouillot et al. (2014) <doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009) <doi:10.1016/j.tpb.2009.10.001>. Graphical outputs are included. Visit the mFD website for more information, documentation and examples.

We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.

With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The MOQA package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.

This package provides a comprehensive and computationally fast framework to analyze high dimensional data associated with an experimental design based on Multiple ANOVAs (MultANOVA). It includes testing the overall significance of terms in the model, post-hoc analyses of significant terms and variable selection. Details may be found in Mahieu, B., & Cariou, V. (2025). MultANOVA Followed by Post Hoc Analyses for Designed Highâ Dimensional Data: A Comprehensive Framework That Outperforms ASCA, rMANOVA, and VASCA. Journal of Chemometrics, 39(7). <doi:10.1002/cem.70039>.

Comprehensive analytical tools are provided to characterize infectious disease superspreading from contact tracing surveillance data. The underlying theoretical frameworks of this toolkit include branching process with transmission heterogeneity (Lloyd-Smith et al. (2005) <doi:10.1038/nature04153>), case cluster size distribution (Nishiura et al. (2012) <doi:10.1016/j.jtbi.2011.10.039>, Blumberg et al. (2014) <doi:10.1371/journal.ppat.1004452>, and Kucharski and Althaus (2015) <doi:10.2807/1560-7917.ES2015.20.25.21167>), and decomposition of reproduction number (Zhao et al. (2022) <doi:10.1371/journal.pcbi.1010281>).

Matching algorithm based on network-flow structure. Users are able to modify the emphasis on three different optimization goals: two different distance measures and the number of treated units left unmatched. The method is proposed by Pimentel and Kelz (2019) <doi:10.1080/01621459.2020.1720693>. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

Multiplicative AR(1) with Seasonal is a stochastic process model built on top of AR(1). The package provides the following procedures for MAR(1)S processes: fit, compose, decompose, advanced simulate and predict.

This package provides functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1) "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications.

Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.

Matching longitudinal methodology models with complex sampling design. It fits fixed and random effects models and covariance structured models so far. It also provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." <https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.

To create maps from tiles, maptiles downloads, composes and displays tiles from a large number of providers (e.g. OpenStreetMap', Stadia', Esri', CARTO', or Thunderforest').