PyHasse, a Software Package for Applicational Studies of Partial Orderings
307
Grisoni, F., Consonni, V., Nembri, S., & Todeschini, R. (2015). How to weight Hasse matrices and
reduce incomparabilities. Chemometrics and Intelligent Laboratory Systems, 147, 95–104.
Halfon, E. (2006). Hasse diagrams and software development. In R. Bruggemann & L. Carlsen
(Eds.), Partial order in environmental sciences and chemistry (pp. 385–392). Berlin: Springer.
Kerber, A. (2017a). Evaluation and exploration, a problem-oriented approach. Toxicological &
Environmental Chemistry, 99, 1270–1282.
Kerber, A. (2017b). Evaluation, considered as problem orientable mathematics over lattices. In
R. B. Marco Fattore (Ed.), Partial order concepts in applied sciences (pp. 87–103). Cham:
Springer.
Koppatz, P., & Bruggemann, R. (2017). PyHasse and cloud computing. In M. Fattore & R.
Bruggemann (Eds.), Partial order concepts in applied sciences (pp. 291–300). Cham: Springer.
Newlin, J., & Patil, G. P. (2010). Application of partial order to stream channel assessment at
bridge infrastructure for mitigation management. Environmental and Ecological Statistics, 17,
437–454.
Patil, G. P., & Taillie, C. (2004). Multiple indicators, partially ordered sets, and linear extensions:
Multi-criterion ranking and prioritization. Environmental and Ecological Statistics, 11, 199–
228.
Todeschini, R., Grisoni, F., & Nembri, S. (2015). Weighted power-weakness ratio for multicriteria
decision making. Chemometrics and Intelligent Laboratory Systems, 146, 329–336.
Von Löwis, M., & Fischbeck, N. (2001). Python 2 – Einführung und Referenz der objektorientierten Skriptsprache. München: Addison-Wesley.
Van de Walle, B., De Baets, B., & Kerre, E. (1998). Characterizable fuzzy preference structures.Annals of Operations Research, 80, 105–136
Weigend, M. (2003). Python – Ge-packt. Bonn: mitp-Verlag.
Weigend, M. (2006). Objektorientierte Programmierung mit Python. Bonn: mitp-Verlag.
307
Grisoni, F., Consonni, V., Nembri, S., & Todeschini, R. (2015). How to weight Hasse matrices and
reduce incomparabilities. Chemometrics and Intelligent Laboratory Systems, 147, 95–104.
Halfon, E. (2006). Hasse diagrams and software development. In R. Bruggemann & L. Carlsen
(Eds.), Partial order in environmental sciences and chemistry (pp. 385–392). Berlin: Springer.
Kerber, A. (2017a). Evaluation and exploration, a problem-oriented approach. Toxicological &
Environmental Chemistry, 99, 1270–1282.
Kerber, A. (2017b). Evaluation, considered as problem orientable mathematics over lattices. In
R. B. Marco Fattore (Ed.), Partial order concepts in applied sciences (pp. 87–103). Cham:
Springer.
Koppatz, P., & Bruggemann, R. (2017). PyHasse and cloud computing. In M. Fattore & R.
Bruggemann (Eds.), Partial order concepts in applied sciences (pp. 291–300). Cham: Springer.
Newlin, J., & Patil, G. P. (2010). Application of partial order to stream channel assessment at
bridge infrastructure for mitigation management. Environmental and Ecological Statistics, 17,
437–454.
Patil, G. P., & Taillie, C. (2004). Multiple indicators, partially ordered sets, and linear extensions:
Multi-criterion ranking and prioritization. Environmental and Ecological Statistics, 11, 199–
228.
Todeschini, R., Grisoni, F., & Nembri, S. (2015). Weighted power-weakness ratio for multicriteria
decision making. Chemometrics and Intelligent Laboratory Systems, 146, 329–336.
Von Löwis, M., & Fischbeck, N. (2001). Python 2 – Einführung und Referenz der objektorientierten Skriptsprache. München: Addison-Wesley.
Van de Walle, B., De Baets, B., & Kerre, E. (1998). Characterizable fuzzy preference structures.Annals of Operations Research, 80, 105–136
Weigend, M. (2003). Python – Ge-packt. Bonn: mitp-Verlag.
Weigend, M. (2006). Objektorientierte Programmierung mit Python. Bonn: mitp-Verlag.