model present in all functions of this package. The argument model is expected to be either a
funtion or a predictor (i.e. an object with a predict function such as
lm).
model = m where m is a function, it will be invoked
once by y <- m(X).
model = m where m is a predictor, it will be invoked
once by y <- predict(m, X).
X is the design of experiments, i.e. a data.frame with
p columns (the input factors) and n lines (each, an
experiment), and y is the vector of length n of the
model responses. The model in invoked once for the whole design of experiment. The argument model can be left to NULL. This is refered to as
the decoupled approach and used with external computational codes that rarely
run on the statistician's computer. See decoupling.src), PCC and PRCC (pcc);
sb);
morris);
PoincareConstant) and (PoincareOptimal),
delsa);
sobolSmthSpl),
sobol),
sobolSalt),
sobol2002),
sobol2007),
soboljansen),
sobolmartinez and soboltouati),
sobolEff),
sobolmara),
sobolowen),
sobolTIIlo) and pick-freeze scheme (Fruth et al., 2014) (sobolTIIpf),
fast99),
sobolroalhs),
sobolroauc),
sobolGP);
shapleyPermEx)
shapleyPermRand)
support) of Fruth et al. (2015);
sensiFdiv) (particular cases: Borgonovo's indices and mutual-information based indices) and Hilbert-Schmidt Independence Criterion (sensiHSIC) of Da Veiga (2015);
PLI) of Lemaitre et al. (2015) and (PLIquantile) of Sueur et al. (2016);
sobolMultOut): Aggregated Sobol' indices (Lamboni et al., 2011; Gamboa et al., 2014) and functional (1D) Sobol' indices.
Moreover, some utilities are provided: standard test-cases
(testmodels) and template file generation
(template.replace).
B. Iooss and A. Saltelli, 2017, Introduction: Sensitivity analysis. In: Springer Handbook on Uncertainty Quantification, R. Ghanem, D. Higdon and H. Owhadi (Eds), Springer. hrefhttp://link.springer.com/referenceworkentry/10.1007/978-3-319-11259-6_31-1
B. Iooss and P. Lemaitre, 2015, A review on global sensitivity analysis methods. In Uncertainty management in Simulation-Optimization of Complex Systems: Algorithms and Applications, C. Meloni and G. Dellino (eds), Springer.
A. Saltelli, K. Chan and E. M. Scott eds, 2000, Sensitivity Analysis, Wiley.
A. Saltelli et al., 2008, Global Sensitivity Analysis: The Primer, Wiley