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tmatta
lsasim:Functions to Facilitate the Simulation of Large Scale Assessment Data
Provides functions to simulate data from large-scale educational assessments, including background questionnaire data and cognitive item responses that adhere to a multiple-matrix sampled design. The theoretical foundation can be found on Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018) <doi:10.1186/s40536-018-0068-8>.
Maintained by Waldir Leoncio. Last updated 2 months ago.
6 stars 6.41 score 18 scripts
mbinois
hetGP:Heteroskedastic Gaussian Process Modeling and Design under Replication
Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Maintained by Mickael Binois. Last updated 7 months ago.
5 stars 4.89 score 260 scripts 2 dependents