Java library for diffusion trajectory (2D) analysis.
- Diffusion coefficient via covariance estimator 
- Diffusion coefficient via regression estimator
- Hydrodynamic diameter by Stokes-Einstein converter
- Aspect ratio
- Asymmetry features 
- Center of gravity
- Efficency 
- Exponent in power law fit to MSD curve 
- Fractal path dimension 
- Gaussianity 
- Kurtosis 
- Maximum distance between two positions
- Maximum distance for given timelag
- Mean speed 
- Mean squared displacment curve curvature 
- Mean squared displacment
- Short-time long-time diffusion coefficent ratio
- Skeweness 
- Spline curve analysis features according to 
- Standard deviation in direction
- Trapped probability 
- Brownian motion (free diffusion)
- Active Transport
- Confined diffusion
- Anomalous diffusion with fixed obstacles (spheres)
- Anomalous diffusion by weierstrass-mandelbrot approach 
- Global linear drift calculator
- Static drift corrector
- Trajectories are combineable
#Maven artifacts TraJ can be found on maven central:
<dependency> <groupId>de.biomedical-imaging.TraJ</groupId> <artifactId>traj</artifactId> <version>MOST RECENT RELEASE</version> </dependency>
 C. L. Vestergaard, P. C. Blainey, and H. Flyvbjerg, “Optimal estimation of diffusion coefficients from single-particle trajectories,” Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys., vol. 89, no. 2, p. 022726, Feb. 2014.
 M. J. Katz and E. B. George, “Fractals and the analysis of growth paths,” Bull. Math. Biol., vol. 47, no. 2, pp. 273–286, 1985.
 S. Huet, E. Karatekin, V. S. Tran, I. Fanget, S. Cribier, and J.-P. Henry, “Analysis of transient behavior in complex trajectories: application to secretory vesicle dynamics.,” Biophys. J., vol. 91, no. 9, pp. 3542–3559, 2006.
 D. Arcizet, B. Meier, E. Sackmann, J. O. Rädler, and D. Heinrich, “Temporal analysis of active and passive transport in living cells,” Phys. Rev. Lett., vol. 101, no. 24, p. 248103, Dec. 2008.
 Spatial structur analysis of diffusive dynamics according to: B. R. Long and T. Q. Vu, “Spatial structure and diffusive dynamics from single-particle trajectories using spline analysis,” Biophys. J., vol. 98, no. 8, pp. 1712–1721, 2010.
 Helmuth, J.A. et al., 2007. A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells. Journal of structural biology, 159(3), pp.347–58.
 Saxton, M.J., 1993. Lateral diffusion in an archipelago. Single-particle diffusion. Biophysical Journal, 64(6), pp.1766–1780.
 Guigas, G. & Weiss, M., 2008. Sampling the Cell with Anomalous Diffusion—The Discovery of Slowness. Biophysical Journal, 94(1), pp.90–94.
 Ernst, D., Köhler, J. & Weiss, M., 2014. Probing the type of anomalous diffusion with single-particle tracking. Physical chemistry chemical physics : PCCP, 16(17), pp.7686–91.
 Helmuth, J.A. et al., 2007., A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells., Journal of structural biology, 159(3), pp.347–58.
 Meijering, Erik; Dzyubachyk, Oleh; Smal, Ihor (2012): „Methods for Cell and Particle Tracking“. In: Imaging and Spectroscopic Analysis of Living Cells - Optical and Spectroscopic Techniques., S. 183-200, DOI: 10.1016/b978-0-12-391857-4.00009-4.
- Size distribution estimation for trajectory sets according to: J. G. Walker, “Improved nano-particle tracking analysis,” Meas. Sci. Technol., vol. 23, no. 6, p. 065605, Jun. 2012. (Already implemented in NanoTrackJ - I just have to port it)
- Simulation: Add anomalous diffusion with brownian motion obstacles and Ornstein-Uhlenbeck obstacles