Differentially Private Statistical Releases for Privacy Preservation

amsweep | Sweep operator ###Check if we need to assign authorship to... |

binaryTree | Function to evaluate a binary tree |

boot.hist | Bootstrap replication for histogram |

boot.mean | Mean function |

bootstrap.replication | Bootstrap replication for a function |

censordata | Censoring data |

checkepsilon | Epsilon Parameter Check |

check_histogram_bins | Check histogram bins argument |

check_histogram_categorical | Utility function to include NA level for categorical types... |

check_histogram_mechanism | Utility function to verify the type of histogram mechanism |

check_histogram_n | Histogram N check |

checkrange | Range Parameter Check |

check_variable_type | Checking variable types |

coefficient.release | Release additional model coefficients from DP covariance... |

covariance.formatRelease | Function to convert unique private covariances into symmetric... |

covariance.postLinearRegression | Function to perform linear regression using private release... |

covariance.sensitivity | Function to get the sensitivity of the covariance matrix |

createfields | Create fields that are known based on type |

createJSON | Function to create JSON file defining differentially private... |

dlap | Probability density for Laplace distribution |

dpCovariance-class | Differentially private covariance matrix |

dpGLM-class | Differentially private generalized linear models |

dpHeavyHitters-class | Differentially private heavy hitters |

dpHistogram-class | Differentially private histogram |

dpLogit | Differentially private objective function for Logistic... |

dpMean-class | Differentially private mean |

dpNoise | Differentially Private Noise Generator |

dpOLS | Differentially private objective function for linear... |

dpPoisson | Differentially private objective function for Poisson... |

dpProbit | Differentially private objective function for Probit... |

dpTree-class | Differentially private binary tree |

dpUnif | Differentially Private Uniform Draw |

dpVariance-class | Differentially private variance |

estAbove | Function to estimate the nodes of a tree using noisy parent... |

estBelow | Function to estimate the nodes of a tree using noisy child... |

estBottomUp | Function to estimate a noisy binary tree from the terminal... |

estEfficient | Function to efficiently estimate the nodes of a tree using... |

estEfficiently | Function to estimate a noisy binary tree efficiently using... |

estTopDown | Function to estimate a noisy binary tree from the top down |

fillfields | Fill in any fields available from release |

fillMissing | Fill missing values |

fillMissing2d | Fill missing values column-wise for matrix |

fun.covar | Lower triangle of ovariance matrix |

fun.heavy | Heavyhitters function |

fun.hist | Histogram |

getFuncArgs | Extract function arguments |

glm.getAccuracy | Accuracy of the differentially private GLM |

glm.getParameters | Privacy parameters for GLM |

glmObjectives | Objective functions |

glm.postSummary | Summary statistics for differentially private GLM via the... |

heavyhitters.getAccuracy | Heavyhitters accuracy |

heavyhitters.getParameters | Heavyhitters epsilon |

histogram.compose | Constrain the sum of histogram bins to sample size |

histogram.formatRelease | Format the release of private histogram |

histogram.getAccuracy | Histogram accuracy |

histogram.getCI | Histogram confidence interval |

histogram.getJSON | JSON doc for histogram |

histogram.getParameters | Histogram epsilon |

histogram.postHerfindahl | Histogram Herfindahl Index |

linear.reg | Linear regression on covariance matrix |

makeDummies | Function to convert factor variables to binary indicators |

make_logical | Logical variable check |

mapMatrixUnit | Function to map rows of a numeric matrix to the unit ball |

mean.getAccuracy | Mean accuracy |

mean.getCI | Mean confidence interval |

mean.getJSON | JSON doc for mean |

mean.getParameters | Mean epsilon |

mean.postHistogram | Postprocessed histogram for logical variables |

mean.postMedian | Postprocessed median for logical variables |

mean.postStandardDeviation | Postprocessed standard deviation for logical variables |

mechanismBootstrap-class | Bootstrap mechanism |

mechanism-class | Base mechanism class |

mechanismExponential-class | Exponential mechanism |

mechanismGaussian-class | Gaussian mechanism |

mechanismLaplace-class | Laplace mechanism |

mechanismObjective-class | Objective perturbation mechanism |

mpinv | Moore Penrose Inverse Function ###Must assign authorship to... |

plap | LaPlace Cumulative Distribution Function |

PSIlence-package | Differentially Private Statistical Releases for Privacy... |

PUMS5extract10000 | Census California Public Use Micro Sample (PUMS) Dataset |

qlap | Quantile function for Laplace distribution |

release2json | Create json file of metadata from list of release objects |

rlap | Random draw from Laplace distribution |

scaleRelease | Scale coefficient estimates |

sgn | Sign function |

stErr | Function to evaluate the standard error of a node estimate... |

tree.getAccuracy | Accuracy for a differentially private binary tree |

tree.getParameters | Epsilon for a differentially private binary tree |

tree.postCDF | Function to derive CDF from efficient terminal node counts |

tree.postEfficient | Function to efficiently estimate noisy node counts |

tree.postFormatRelease | Function to truncate negative noisy node counts at zero |

tree.postMean | Function to evaluate the mean using the DP CDF |

tree.postMedian | Function to evaluate the median using the DP CDF |

tree.postPercentiles | Quantile function using the DP CDF |

trimVector | Function to trim lower and upper regions of a vector of... |

variance.postStandardDeviation | Postprocessed variance standard deviation |

vectorNorm | Function to evaluate the p-norm of vectors in a matrix |

wAbove | Function to evaluate weights from the noise variance and... |

wBelow | Function to evaluate weights from the noise variance and... |

wEfficient | Function to evaluate weights efficiently using the noise... |

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