Graphical Tools of Discrete Longitudinal Data Presentation in R

Billari F.C., 2001, The analysis of early life courses: Complex description of the transition to adulthood, Journal of Population Research, 18(2), pp. 119-142.10.1007/BF03031885Search in Google Scholar

Brzinsky-Fay C., Kohler U., Luniak M., 2006, Sequence analysis with stata, The Stata Journal, 6(4), pp. 435-460.10.1177/1536867X0600600401Open DOISearch in Google Scholar

Elzinga C.H., Liefbroer A.C., 2007, De-standardization of family-life trajectories of young adults: A cross-national comparison using sequence analysis, European Journal of Population, 23, 225-250, https://link.springer.com/content/pdf/10.1007%2Fs10680-007-9133-7.pdf.10.1007/s10680-007-9133-7Search in Google Scholar

Gabadinho A., Ritschard G., Müller N.S., Studer M., 2010, Indice de complexité pour le tri et la comparison de séquences catégorielles, Revue des nouvelles technlologies de l’information RNTI, E-19, pp. 61-66.Search in Google Scholar

Gabadinho A., Ritschard G., Müller N.S., Studer M., 2011, Analyzing and visualizing state sequences in R with TraMineR, Journal of Statistical Software, 40(4), pp. 1-37.10.18637/jss.v040.i04Open DOISearch in Google Scholar

Gabadinho A., Studer M., Müller N., Bürgin R., Fonta P.A., Ritschard G., 2017, TraMineR – Trajectory Miner: A Toolbox for Exploring and Rendering Sequence, Version 2.0-7, https://cran.r-project.org/web/packages/TraMineR/TraMineR.pdf.Search in Google Scholar

Helske J., Helske S., 2017, Hidden Markov Models for live sequences and other multi-variate multi-channel categorical time series, Version 1.0.8, https://cran.r-project.org/web/packages/seqHMM/seqHMM.pdf.Search in Google Scholar

Muthén B., Shedden K., 1999, Finite mixture modeling with mixture outcomes using the EM algorithm, Biometrics, 55(2), pp. 463-469.10.1111/j.0006-341X.1999.00463.x11318201Open DOISearch in Google Scholar

Ritschard G., Gabadinho A., Studer M., Müller N.S., 2009, Converting between Various Sequence Representations, [in:] Z. Ras, A. Dardzinska, Advances in Data Management, Studies in Computational Intelligence, 223 Springer-Verlag, Berlin, pp. 155-175, DOI:10.1007/978-3-642-02190-9\_8.10.1007/978-3-642-02190-9\_8Open DOISearch in Google Scholar

Scherer S., 2001, Early career patterns: a comparison of Great Britain and West Germany, European Sociological Review, 17(2), pp. 119-144.10.1093/esr/17.2.119Open DOISearch in Google Scholar

Singer J.D., Willett J.B., 2003, Applied longitudinal data analysis: Modeling change and event occurrence, Oxford, UK, Oxford University Press.10.1093/acprof:oso/9780195152968.001.0001Search in Google Scholar

Social Diagnosis, 2015, Objective and Subjective Quality of Life in Poland, Czapinski J., Panek T. (eds.), Warszawa, Social Monitoring Council (22.11.2017), http://www.diagnoza.com/index-en.html.Search in Google Scholar

Tueller S.J., 2017, longCatEDA – Package for Plotting Categorical Longitudinal and Time-Series. Version 0.31, https://cran.r-project.org/web/packages/longCatEDA/longCatEDA.pdf.10.3768/rtipress.2016.mr.0033.1602Search in Google Scholar

Tueller S.J., Dorn R.A., Bobashev G.V., 2016, longCatEDA: Package for Plotting Categorical Longitudinal and Time-Series Data, Methods Report RTI Press. 2016 Feb; 2016: MR-0033-1602, DOI: 10.3768/rtipress.2016.mr.0033.1602.10.3768/rtipress.2016.mr.0033.1602Open DOISearch in Google Scholar