Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

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mod2rm — by Matthias Forstmann, 4 years ago

Moderation Analysis for Two-Instance Repeated Measures Designs

Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" .

RcppGreedySetCover — by Matthias Kaeding, 4 months ago

Greedy Set Cover

A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.

StMoSim — by Matthias Salvisberg, 4 months ago

Quantile-Quantile Plot with Several Gaussian Simulations

Plots a QQ-Norm Plot with several Gaussian simulations.

RobRex — by Matthias Kohl, 7 years ago

Optimally Robust Influence Curves for Regression and Scale

Functions for the determination of optimally robust influence curves in case of linear regression with unknown scale and standard normal distributed errors where the regressor is random.

RobLoxBioC — by Matthias Kohl, a year ago

Infinitesimally Robust Estimators for Preprocessing -Omics Data

Functions for the determination of optimally robust influence curves and estimators for preprocessing omics data, in particular gene expression data (Kohl and Deigner (2010), ).

ROptEstOld — by Matthias Kohl, 7 years ago

Optimally Robust Estimation - Old Version

Optimally robust estimation using S4 classes and methods. Old version still needed for current versions of ROptRegTS and RobRex.

VeccTMVN — by Jian Cao, 2 months ago

Multivariate Normal Probabilities using Vecchia Approximation

Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" . Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" .

panelaggregation — by Matthias Bannert, 9 years ago

Aggregate Longitudinal Survey Data

Aggregate Business Tendency Survey Data (and other qualitative surveys) to time series at various aggregation levels. Run aggregation of survey data in a speedy, re-traceable and a easily deployable way. Aggregation is substantially accelerated by use of data.table. This package intends to provide an interface that is less general and abstract than data.table but rather geared towards survey researchers.

origin — by Matthias Braun FKA Nistler, 3 months ago

Explicitly Qualifying Namespaces by Automatically Adding 'pkg::' to Functions

Automatically adding 'pkg::' to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the 'RStudio' Markers pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used for checking which packages are actually used in a project.

CBAModel — by Matthias Neumann, a year ago

Stochastic 3D Structure Model for Binder-Conductive Additive Phase

Simulation of the stochastic 3D structure model for the nanoporous binder-conductive additive phase in battery cathodes introduced in P. Gräfensteiner, M. Osenberg, A. Hilger, N. Bohn, J. R. Binder, I. Manke, V. Schmidt, M. Neumann (2024) . The model is developed for a binder-conductive additive phase of consisting of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains.