Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.

Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.

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, 2021 and 2023) <doi:10.1080/14686996.2019.1620123>, <doi:10.1080/27660400.2021.1899449> <doi:10.1080/27660400.2022.2159753>. 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.

Tools for exploratory analysis of tabular data using colour 
 highlighting. Highlighting is displayed in any console supporting 'ANSI'
 colours, and can be converted to 'HTML', 'typst', 'latex' and 'SVG'. 
 'quarto' and 'rmarkdown' rendering are directly supported. It is 
 also possible to add colour to regular expression matches and 
 highlight differences between two arbitrary R objects.

Evaluates the empirical characteristic function of univariate and multivariate samples.
    This package uses 'RcppArmadillo' for fast evaluation. It is also possible to export the code to be used in other packages at 'C++' level.

Routines for performing empirical calibration of observational
  study estimates. By using a set of negative control hypotheses we can
  estimate the empirical null distribution of a particular observational
  study setup. This empirical null distribution can be used to compute a
  calibrated p-value, which reflects the probability of observing an
  estimated effect size when the null hypothesis is true taking both random
  and systematic error into account. A similar approach can be used to
  calibrate confidence intervals, using both negative and positive controls. 
  For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and
  Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.