Agreement of Nominal Scale Raters (with a Gold Standard)

Estimate agreement of a group of raters with a gold standard rating on a nominal scale. For a single gold standard rater the average pairwise agreement of raters with this gold standard is provided. For a group of (gold standard) raters the approach of S. Vanbelle, A. Albert (2009) is implemented. Bias and standard error are estimated via delete-1 jackknife.

Easy Computation of Functional Diversity Indices

Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 ). Provides efficient, modular, and parallel functions to compute functional diversity indices (preprint: ).

Dynamic Path Analysis of Survival Data via Aalen's Additive Hazards Model

Dynamic path analysis with estimation of the corresponding direct, indirect, and total effects, based on Fosen et al., (2006) . The main outcome of interest is a counting process from survival analysis (or recurrent events) data. At each time of event, ordinary linear regression is used to estimate the relation between the covariates, while Aalen's additive hazard model is used for the regression of the counting process on the covariates.

Fitting Distributions to Given Data or Known Quantiles

Collection of functions for fitting distributions to given data or by known quantiles. Two main functions fit.perc() and fit.cont() provide users a GUI that allows to choose a most appropriate distribution without any knowledge of the R syntax. Note, this package is a part of the 'rrisk' project.

Estimation and Testing Classes Based on Package 'distr'

Evaluation (S4-)classes based on package distr for evaluating procedures (estimators/tests) at data/simulation in a unified way.

Simulation Classes Based on Package 'distr'

S4-classes for setting up a coherent framework for simulation within the distr family of packages.

Scalable Gaussian-Process Approximations

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) . Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) and MaxMin ordering proposed in Guinness (2018) .

Infrastructure for Data Stream Mining

A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) .

Daten, Beispiele und Funktionen zu 'Large-Scale Assessment mit R'

Dieses R-Paket stellt Zusatzmaterial in Form von Daten, Funktionen und R-Hilfe-Seiten für den Herausgeberband Breit, S. und Schreiner, C. (Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung." Wien: facultas. (ISBN: 978-3-7089-1343-8, < https://www.iqs.gv.at/themen/bildungsforschung/publikationen/veroeffentlichte-publikationen>) zur Verfügung.

Analyzing Interval Data in Psychometrics

Implements the Interval Consensus Model (ICM) for analyzing continuous bounded interval-valued responses in psychometrics using 'Stan' for 'Bayesian' estimation. Provides functions for transforming interval data to simplex representations, fitting item response theory (IRT) models with isometric log-ratio (ILR) and sum log-ratio (SLR) link functions, and visualizing results. The package enables aggregation and analysis of interval-valued response data commonly found in psychological measurement and related disciplines. Based on Kloft et al. (2024) .