Description: Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").

Title: Conducting the Peak Fitting Based on the EM Algorithm
Authors@R: person("Tarojiro", "Matsumura", role = c("aut", "cre"), email = "matsumura-tarojiro@aist.go.jp")
Description: The peak fitting of spectral data is performed by using the frame work of EM algorithm.     We adapted the EM algorithm for the peak fitting of spectral data set by considering     the weight of the intensity corresponding to the measurement energy steps    (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019) <doi:10.1080/14686996.2019.1620123>).    The package efficiently estimates the parameters of Gaussian mixture model during    iterative calculation between E-step and M-step, and    the parameters are converged to a local optimal solution.    This package can support the investigation of peak shift with two advantages:    (1) a large amount of data can be processed at high speed; and   (2) stable and automatic calculation can be easily performed.
Packaged: 2021-04-20 12:59:10 UTC; tarojiro.matsumura

Description: The peak fitting of spectral data is performed by using the frame work of EM algorithm.     We adapted the EM algorithm for the peak fitting of spectral data set by considering     the weight of the intensity corresponding to the measurement energy steps    (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019 and 2021) <doi:10.1080/14686996.2019.1620123> and <doi:10.1080/27660400.2021.1899449>.    The package efficiently estimates the parameters of Gaussian mixture model during    iterative calculation between E-step and M-step, and    the parameters are converged to a local optimal solution.    This package can support the investigation of peak shift with two advantages:    (1) a large amount of data can be processed at high speed; and   (2) stable and automatic calculation can be easily performed.
Packaged: 2021-12-20 13:50:26 UTC; tarojiro.matsumura