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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.
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)
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)
Quantile-Quantile Plot with Several Gaussian Simulations
Plots a QQ-Norm Plot with several Gaussian simulations.
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)
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).
Boosting Methods for 'GAMLSS'
Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.
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).