The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
A fuzzy number is the alpha-cut envelope
[X^-(alpha), X^+(alpha)] with a non-decreasing lower and
non-increasing upper endpoint.
A FuzzySample is an (n, m, 2) array of cuts
for n i.i.d. fuzzy random variables observed on
m alpha levels.
expectation(s)
#> <FuzzyNumber>
#> levels: 51 (alpha 0 .. 1)
#> support : [ 0 , 2 ]
#> core : [ 1 , 1 ]
variance(s)
#> <FuzzyCovariance>
#> type : variance
#> levels : 51
#> scalar : 0
#> at levels alpha = 0, 0.5, 1:
#> Var(lo) Cov(lo,up) Cov(up,lo) Var(up)
#> 0.0 0 0 0 0
#> 0.5 0 0 0 0
#> 1.0 0 0 0 0
correlation(s, FuzzySample(replicate(60, triangular_fuzzy(1, 2, 3),
simplify = FALSE)))
#> <FuzzyCorrelation>
#> levels : 51
#> scalar : 0
#> at levels alpha = 0, 0.5, 1:
#> [,1] [,2] [,3]
#> lower NA NA NA
#> upper NA NA NAThe empirical CDFs and a level-wise Kolmogorov–Smirnov test against a reference distribution:
fuzzy_normal() builds a level-wise bivariate-normal
fuzzy number. clt() applies the level-wise CLT to a sample,
and simulate() draws from it:
cl <- clt(s)
summary(cl)
#> <summary FuzzyCLT>
#> alpha mean_lower mean_upper var_lower var_upper cov_cross scalar scalar_limit
#> 0.00 0.00 2.00 0 0 0 0 0
#> 0.02 0.02 1.98 0 0 0 0 0
#> 0.04 0.04 1.96 0 0 0 0 0
#> 0.06 0.06 1.94 0 0 0 0 0
#> 0.08 0.08 1.92 0 0 0 0 0
#> 0.10 0.10 1.90 0 0 0 0 0
#> 0.12 0.12 1.88 0 0 0 0 0
#> 0.14 0.14 1.86 0 0 0 0 0
#> 0.16 0.16 1.84 0 0 0 0 0
#> 0.18 0.18 1.82 0 0 0 0 0
#> 0.20 0.20 1.80 0 0 0 0 0
#> 0.22 0.22 1.78 0 0 0 0 0
#> 0.24 0.24 1.76 0 0 0 0 0
#> 0.26 0.26 1.74 0 0 0 0 0
#> 0.28 0.28 1.72 0 0 0 0 0
#> 0.30 0.30 1.70 0 0 0 0 0
#> 0.32 0.32 1.68 0 0 0 0 0
#> 0.34 0.34 1.66 0 0 0 0 0
#> 0.36 0.36 1.64 0 0 0 0 0
#> 0.38 0.38 1.62 0 0 0 0 0
#> 0.40 0.40 1.60 0 0 0 0 0
#> 0.42 0.42 1.58 0 0 0 0 0
#> 0.44 0.44 1.56 0 0 0 0 0
#> 0.46 0.46 1.54 0 0 0 0 0
#> 0.48 0.48 1.52 0 0 0 0 0
#> 0.50 0.50 1.50 0 0 0 0 0
#> 0.52 0.52 1.48 0 0 0 0 0
#> 0.54 0.54 1.46 0 0 0 0 0
#> 0.56 0.56 1.44 0 0 0 0 0
#> 0.58 0.58 1.42 0 0 0 0 0
#> 0.60 0.60 1.40 0 0 0 0 0
#> 0.62 0.62 1.38 0 0 0 0 0
#> 0.64 0.64 1.36 0 0 0 0 0
#> 0.66 0.66 1.34 0 0 0 0 0
#> 0.68 0.68 1.32 0 0 0 0 0
#> 0.70 0.70 1.30 0 0 0 0 0
#> 0.72 0.72 1.28 0 0 0 0 0
#> 0.74 0.74 1.26 0 0 0 0 0
#> 0.76 0.76 1.24 0 0 0 0 0
#> 0.78 0.78 1.22 0 0 0 0 0
#> 0.80 0.80 1.20 0 0 0 0 0
#> 0.82 0.82 1.18 0 0 0 0 0
#> 0.84 0.84 1.16 0 0 0 0 0
#> 0.86 0.86 1.14 0 0 0 0 0
#> 0.88 0.88 1.12 0 0 0 0 0
#> 0.90 0.90 1.10 0 0 0 0 0
#> 0.92 0.92 1.08 0 0 0 0 0
#> 0.94 0.94 1.06 0 0 0 0 0
#> 0.96 0.96 1.04 0 0 0 0 0
#> 0.98 0.98 1.02 0 0 0 0 0
#> 1.00 1.00 1.00 0 0 0 0 0
plot(simulate(cl, nsim = 80))
lln() studies the shrinkage of the sample-mean width
against sample size:
w <- lln(s)
plot(w)
#> Warning in xy.coords(x, y, xlabel, ylabel, log): 8 y values <= 0 omitted from
#> logarithmic plot
#> Warning in plot.window(...): nonfinite axis=2 limits [GScale(-inf,-inf,..);
#> log=TRUE] -- corrected now
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.