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) .

Tools to Analyse RFLP Data

Provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).

Fit and Deploy DECORATE Trees

DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples) builds an ensemble of J48 trees by recursively adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005) ").

Embed 'SWI'-'Prolog'

Interface to 'SWI'-'Prolog', < https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.

Reference Interval Estimation using Real-World Data

Indirect method for the estimation of reference intervals (RIs) using Real-World Data ('RWD') and methods for comparing and verifying RIs. Estimates RIs by applying advanced statistical methods to routine diagnostic test measurements, which include both pathological and non-pathological samples, to model the distribution of non-pathological samples. This distribution is then used to derive reference intervals and support RI verification, i.e., deciding if a specific RI is suitable for the local population. The package also provides functions for printing and plotting algorithm results. See ?refineR for a detailed description of features. Version 1.0 of the algorithm is described in 'Ammer et al. (2021)' . Additional guidance is in 'Ammer et al. (2023)' . The verification method is described in 'Beck et al. (2025)' .

Closed Testing Procedure (CTP)

This is a package for constructing hypothesis trees for treatment comparisons based on the closure principle and analysing the corresponding Closed Testing Procedures (CTP) using adjusted p-values. For reference, see Marcus, R., Peritz, E, and Gabriel, K.R. (1976) and Bauer, P (1991) .

Draw Samples of Truncated Multivariate Normal Distributions

Draw samples from truncated multivariate normal distribution using the sequential nearest neighbor (SNN) method introduced in "Scalable Sampling of Truncated Multivariate Normals Using Sequential Nearest-Neighbor Approximation" .

'Google Ads API' Interface

Interface for the 'Google Ads API'. 'Google Ads' is an online advertising service that enables advertisers to display advertising to web users (see < https://developers.google.com/google-ads/> for more information).

Linear and Smooth Predictor Modelling with Penalisation and Variable Selection

Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model).