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Circular Data Modelling


Published papers:

  • Cremers, J., Pennings, H.J.M., Mainhard, M.T. & Klugkist, I.G. (accepted). Circular Modelling of Circumplex Measurements for Interpersonal Behavior, Assessement.
  • Cremers, J., Mainhard, M.T. & Klugkist, I.G. (2018). Assessing a Bayesian embedding approach to circular regression models. Methodology, 14 (2), 69-81.
  • Cremers, J., Mulder, K.T. & Klugkist, I.G. (2018). Circular interpretation of regression coefficients. British Journal of Mathematical and Statistical Psychology, 71 (1),  75-95.
  • Cremers, J. & Klugkist, I.G. (2018). One Direction? A Tutorial for Circular Data Analysis Using R With Examples in Cognitive Psychology. Frontiers in Psychology, 9.
  • Mulder, K.T. & Klugkist, I.G. (2017). Bayesian estimation and hypothesis tests for a circular Generalized Linear Model. Journal of Mathematical Psychology, 80, 4-14.
  • Ketelaar, J., Cremers, J., Mulder, K. T., Klugkist, I. (2015). Statistiek op de cirkel. STAtOR, 16(3), 24-27.
  • Baayen, C., Klugkist, I. (2014). Evaluating order-constrained hypotheses for circular data from a between-within subjects design. Psychological Methods, 19, 398-408
  • Baayen, C., Klugkist, I., Mechsner, F. (2012). A test of order constrained hypotheses for circular data with applications to human movement science. Journal of Motor Behavior, 44, 351-363
  • Klugkist, I., Bullens, J., Postma, A. (2012). Evaluating order constrained hypotheses for circular data using permutation tests. British Journal of Mathematical and Statistical Psychology, 65, 222-236

Book chapter:

  • Klugkist, I.G., Cremers, J. & Mulder, K.T. (2018). Bayesian Analysis of Circular Data in Social and Behavioural Sciences. In: Ley, C. and Verdebout, T. (eds). Applied Directional Statistics – Modern Methods and Case Studies. New York: Chapman and Hall/CRC.

Papers under review:

  • Cremers, J., Jansen, I., Klugkist, I. (submitted). Bayesian regression for circular data using the wrapping approach.
  • Cremers, J., Pennings, H.J.M., Ley, C. (submitted). Regression models for cylindrical data in psychology
  • Cremers, J., Hernandez-Stumpfhauser, D. (submitted). Indicators for effects on mean and variance in projected normal regression models for a circular outcome
  • Mulder, K.T. & Klugkist, I. (submitted). Bayesian test for circular uniformity.
  • Mulder, K.T., Klugkist, I., van Renswoude, D., Visser, I. (submitted). Mixtures of peaked power Batschelet distributions with application to saccade directions.
  • Mulder, K.T., Jongsma, P., Klugkist, I. (submitted). Bayesian inference for mixtures of von Mises distributions using the reversible jump MCMC sampler.
  • Van der Lans, R., Cremers, J., Klugkist, I., Zwart, R. (submitted). Teachers’ interpersonal relationships and instructional quality: How are they related?

Work in progress:

  • Kleine Bardenhorst, S., Mulder, K.T., Klugkist, I.G. (in preparation). Mediation models with a circular outcome.
  • Mulder, K.T. & Ruiter, S. (in preparation). Inference for interval-censored circular data with application to crime times.
  • Mulder, K.T., Cremers, J., Klugkist, I. (in preparation). circbayes: An R package for Bayesian circular statistics.

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