References in zbMATH (referenced in 41 articles , 1 standard article )

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  1. Peeters, C. F. W., Bilgrau, A. E., van Wieringen, W. N. : rags2ridges: A One-Stop-l2-Shop for Graphical Modeling of High-Dimensional Precision Matrices (2022) not zbMATH
  2. Adrian Richter; Carsten Oliver Schmidt; Markus Krüger; Stephan Struckmann: dataquieR: assessment of data quality in epidemiological research (2021) not zbMATH
  3. Arsalane Chouaib Guidoum, Kamal Boukhetala: Performing Parallel Monte Carlo and Moment Equations Methods for Ito and Stratonovich Stochastic Differential Systems: R Package Sim.DiffProc (2020) not zbMATH
  4. Fernando S. Marques, José H. H. Grisi-Filho, Jean C. R. Silva, Erivânia C. Almeida, José L. Silva Júnior: hybridModels: An R Package for the Stochastic Simulation of Disease Spreading in Dynamic Networks (2020) not zbMATH
  5. Gu, Mengyang; Xu, Yanxun: Fast nonseparable Gaussian stochastic process with application to methylation level interpolation (2020)
  6. Kandanaarachchi, Sevvandi; Muñoz, Mario A.; Hyndman, Rob J.; Smith-Miles, Kate: On normalization and algorithm selection for unsupervised outlier detection (2020)
  7. Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
  8. Neeraj Dhanraj Bokde; Gorm Bruun Andersen: ForecastTB - An R Package as a Test-bench for Forecasting Methods Comparison (2020) arXiv
  9. Talagala, Priyanga Dilini; Hyndman, Rob J.; Smith-Miles, Kate; Kandanaarachchi, Sevvandi; Muñoz, Mario A.: Anomaly detection in streaming nonstationary temporal data (2020)
  10. Chakraborty, Saptarshi; Khare, Kshitij: Consistent estimation of the spectrum of trace class data augmentation algorithms (2019)
  11. Huang, Li-Shan; Yu, Chung-Hsin: Classical backfitting for smooth-backfitting additive models (2019)
  12. Kaplan, Andee J.; Hare, Eric R.: Putting down roots: a graphical exploration of community attachment (2019)
  13. Li, Zehang Richard; McCormick, Tyler H.: An expectation conditional maximization approach for Gaussian graphical models (2019)
  14. Oleksii Pokotylo; Pavlo Mozharovskyi; Rainer Dyckerhoff: Depth and Depth-Based Classification with R Package ddalpha (2019) not zbMATH
  15. Westling, T.; McCormick, T. H.: Beyond prediction: a framework for inference with variational approximations in mixture models (2019)
  16. Klein, Hartmut; Hesse, Linnea; Boljen, Matthias; Kampowski, Tim; Butschek, Irina; Speck, Thomas; Speck, Olga: Finite element modelling of complex movements during self-sealing of ring incisions in leaves of \textitDelospermacooperi (2018)
  17. Philipp, Michel; Rusch, Thomas; Hornik, Kurt; Strobl, Carolin: Measuring the stability of results from supervised statistical learning (2018)
  18. Tan, Teck Kiang: Doubly classified model with R (2017)
  19. Tyralis, Hristos; Papacharalampous, Georgia: Variable selection in time series forecasting using random forests (2017)
  20. Xu, Zheng: Book review of: A. Hector, The new statistics with R. An introduction for biologists (2017)

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