ErlangRenewal: renewal function of gamma (lam, k) interarrivals,
BerksonCorrelation: Berkson's fallacy correlation,
MillsConditionalMean: E(Y | X > x) for a standard bivariate normal,
BivNormDependence: mean-square contingency and mutual information,
MaximalCorrelation: Renyi maximal correlation of a discrete pair,
BetaBinomialPmf: beta-binomial (negative hypergeometric) law,
ArSpectralDensity and AcfSpectralDensity: spectral densities,
TelegraphProcess: two-state Markov autocorrelation and spectrum,
WienerConditionalCorrelation: Brownian-bridge correlation,
SimpleEpidemicDuration: moments of the time to total infection,
StirlingGammaRatio: Stirling's ratio, and MaAutocorrelation:
moving-average autocorrelation. Identical to the Python arm
morie.fn.probresults.
Usage
ErlangRenewal(t, lam, k = 2L)
BerksonCorrelation(gamma, a, c)
MillsConditionalMean(x, rho)
BivNormDependence(rho)
MaximalCorrelation(table)
BetaBinomialPmf(k, n, a, b)
ArSpectralDensity(lam, alpha, sigma2 = 1, normalized = TRUE, ngrid = 4096L)
AcfSpectralDensity(lam, rho)
TelegraphProcess(t, lam, alpha, beta)
WienerConditionalCorrelation(s, t, u, v)
SimpleEpidemicDuration(N, lam)
StirlingGammaRatio(t)
MaAutocorrelation(theta, max_lag)Arguments
- t, s, u, v
Times.
- lam
Rate, or frequency for spectral densities.
- k
Erlang shape, or number of successes for
BetaBinomialPmf.- gamma, a, c
Probabilities of disease, other admission and admission for the disease.
- x
Threshold.
- rho
Correlation, or vector of positive-lag autocorrelations.
- table
Joint counts or probabilities (rows X, columns Y).
- n
Number of trials.
- b
Second beta parameter (with
athe first).- alpha
AR coefficients, or first telegraph rate.
- sigma2
Innovation variance.
- normalized
Divide by the variance so the density integrates to one.
- ngrid
Trapezoid points for the variance.
- beta
Second telegraph rate.
- N
Number initially susceptible.
- theta
MA coefficients.
- max_lag
Largest lag.