Read, register and compare point sets from single molecule localization microscopy.

Computes the Lomb-Scargle Periodogram and actogram for evenly or unevenly sampled time series. Includes a randomization procedure to obtain exact p-values. Partially based on C original by Press et al. (Numerical Recipes) and the Python module Astropy. For more information see Ruf, T. (1999). The Lomb-Scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biological Rhythm Research, 30(2), 178-201.

An easy tool to transform 2D longitudinal data into 3D arrays suitable for 
  Long short-term memory neural networks training. The array output can be
  used by the 'keras' package. Long short-term memory neural networks are described
  in: Hochreiter, S., & Schmidhuber, J. (1997) <doi:10.1162/neco.1997.9.8.1735>.

Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.

Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. Details in McNicholas and Murphy (2010) <doi:10.1002/cjs.10047> and McNicholas and Subedi (2012) <doi:10.1016/j.jspi.2011.11.026>.

This tool takes longitudinal dataset as input and analyzes if there is significant 
             change of the features over time (a proxy for treatments), while detects and controls 
             for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input, 
             including count, proportion, binary, ordinal and continuous data. The output table contains 
              p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.