Tools for testing and improving accuracy of statistical results.

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Documentation for package `accuracy' version 1.06

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anova.perturb Generic methods for perturbations.
chisqtst Benchmark data to test the accuracy of statistical distribution functions, function to compute log relative error
dehaan Function to test that an optimum from MLE, NLS, or other non-linear optization routine is a global optimum
frexp Function to convert vector of floating-point numbers to fractional and integral components
ftst Benchmark data to test the accuracy of statistical distribution functions, function to compute log relative error
gammatst Benchmark data to test the accuracy of statistical distribution functions, function to compute log relative error
initPool Function to return TRUE (not pseudo) random numbers, based on system and networked entropy collection.
LRE Benchmark data to test the accuracy of statistical distribution functions, function to compute log relative error
normtst Benchmark data to test the accuracy of statistical distribution functions, function to compute log relative error
perturb Data Perturbations based Sensitivity Analysis
plot.perturb Generic methods for perturbations.
plot.perturbS Generic methods for perturbations.
print.perturb Generic methods for perturbations.
psim perform zelig simulations for perturbed models
PTBcharacter Function to perturb vectors of discrete numeric values or factors, logicals, characters
PTBdefault Returns a default perturbation function for a given vector.
PTBdefaultfn Returns a default perturbation function for a given vector.
PTBdiscrete Function to perturb vectors of discrete numeric values or factors, logicals, characters
PTBi functions to add random noise to a vector
PTBlogical Function to perturb vectors of discrete numeric values or factors, logicals, characters
PTBmn.gen generator functions for multiple rounds of noise
PTBms functions to add random noise to a vector
PTBmsb functions to add random noise to a vector
PTBmsbr functions to add random noise to a vector
PTBmu.gen generator functions for multiple rounds of noise
PTBn functions to add random noise to a vector
PTBnbr functions to add random noise to a vector
PTBnbrr functions to add random noise to a vector
PTBnc functions to add random noise to a vector
PTBns functions to add random noise to a vector
PTBnsbr functions to add random noise to a vector
PTBnsbrr functions to add random noise to a vector
PTBu functions to add random noise to a vector
PTBubr functions to add random noise to a vector
PTBubrr functions to add random noise to a vector
PTBuc functions to add random noise to a vector
PTBus functions to add random noise to a vector
PTBusbr functions to add random noise to a vector
PTBusbrr functions to add random noise to a vector
pzelig Perturbations-based Sensitivity Analysis of Zelig Models
reclass.mat.diag Function to produce reclassification matrices
reclass.mat.random Function to produce reclassification matrices
resetSeed Function to return TRUE (not pseudo) random numbers, based on system and networked entropy collection.
runifS Function to return TRUE (not pseudo) random numbers, based on system and networked entropy collection.
runifT Function to return TRUE (not pseudo) random numbers, based on system and networked entropy collection.
sechol Schnabel-Eskow Choleksy Decomposition
setx.perturb perform zelig simulations for perturbed models
starr Function to test that an optimum from MLE, NLS, or other non-linear optization routine is a global optimum
summary.perturb Generic methods for perturbations.
trueRandom Function to return TRUE (not pseudo) random numbers, based on system and networked entropy collection.
ttst Benchmark data to test the accuracy of statistical distribution functions, function to compute log relative error