Histogram-Valued Data Analysis
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Documentation for package ‘HistDAWass’ version 1.0.8
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A B C D G H I K M O P R S T W misc
| HistDAWass-package | Histogram-Valued Data Analysis |
-- A --
| Age_Pyramids_2014 | Age pyramids of all the countries of the World in 2014 |
| Agronomique | Agronomique data |
-- B --
| BLOOD | Blood dataset for Histogram data analysis |
| BloodBRITO | Blood dataset from Brito P. for Histogram data analysis |
-- C --
| Center.cell.MatH | Method Center.cell.MatH Centers all the cells of a matrix of distributions |
| Center.cell.MatH-method | Method Center.cell.MatH Centers all the cells of a matrix of distributions |
| checkEmptyBins | Method 'checkEmptyBins' |
| checkEmptyBins-method | Method 'checkEmptyBins' |
| China_Month | A monthly climatic dataset of China |
| China_Seas | A seasonal climatic dataset of China |
| compP | Method 'compP' |
| compP-method | Method 'compP' |
| compQ | Method 'compQ' |
| compQ-method | Method 'compQ' |
| crwtransform | Method 'crwtransform': returns the centers and the radii of bins of a distribution |
| crwtransform-method | Method 'crwtransform': returns the centers and the radii of bins of a distribution |
-- D --
| data2hist | From real data to distributionH. |
| distributionH | Class distributionH. |
| distributionH-class | Class distributionH. |
| dotpW | Method 'dotpW' |
| dotpW-method | Method 'dotpW' |
| DouglasPeucker | Ramer-Douglas-Peucker algorithm for curve fitting with a PolyLine |
-- G --
| get.cell.MatH | Method get.cell.MatH Returns the histogram in a cell of a matrix of distributions |
| get.cell.MatH-method | Method get.cell.MatH Returns the histogram in a cell of a matrix of distributions |
| get.distr | Method 'get.distr': show the distribution |
| get.distr-method | Method 'get.distr': show the distribution |
| get.histo | Method 'get.histo': show the distribution with bins |
| get.histo-method | Method 'get.histo': show the distribution with bins |
| get.m | Method 'get.m': the mean of a distribution |
| get.m-method | Method 'get.m': the mean of a distribution |
| get.MatH.main.info | Method get.MatH.main.info |
| get.MatH.main.info-method | Method get.MatH.main.info |
| get.MatH.ncols | Method get.MatH.ncols |
| get.MatH.ncols-method | Method get.MatH.ncols |
| get.MatH.nrows | Method get.MatH.nrows |
| get.MatH.nrows-method | Method get.MatH.nrows |
| get.MatH.rownames | Method get.MatH.rownames |
| get.MatH.rownames-method | Method get.MatH.rownames |
| get.MatH.stats | Method get.MatH.stats |
| get.MatH.stats-method | Method get.MatH.stats |
| get.MatH.varnames | Method get.MatH.varnames |
| get.MatH.varnames-method | Method get.MatH.varnames |
| get.s | Method 'get.s': the standard deviation of a distribution |
| get.s-method | Method 'get.s': the standard deviation of a distribution |
-- H --
| HistDAWass | Histogram-Valued Data Analysis |
| HTS | Class HTS |
| HTS-class | Class HTS |
| HTS.exponential.smoothing | Smoothing with exponential smoothing of a histogram time series |
| HTS.moving.averages | Smoothing with moving averages of a histogram time series |
| HTS.predict.knn | K-NN predictions of a histogram time series |
-- I --
| initialize-method | Class HTS |
| initialize-method | Class MatH. |
| initialize-method | Class TMatH |
| initialize-method | Class TdistributionH |
| initialize-method | Class distributionH. |
| is.registeredMH | Method is.registeredMH |
| is.registeredMH-method | Method is.registeredMH |
-- K --
| kurtH | Method 'kurtH': computes the kurthosis of a distribution |
| kurtH-method | Method 'kurtH': computes the kurthosis of a distribution |
-- M --
| MatH | Class MatH. |
| MatH-class | Class MatH. |
| meanH | Method 'meanH': computes the mean of a distribution |
| meanH-method | Method 'meanH': computes the mean of a distribution |
| minus | Method - |
-- O --
| OzoneFull | Full Ozone dataset for Histogram data analysis |
| OzoneH | Complete Ozone dataset for Histogram data analysis |
-- P --
| plot-distributionH | plot for a distributionH object |
| plot-HTS | Method plot for a histogram time series |
| plot-MatH | Method plot for a matrix of histograms |
| plot-method | Method plot for a histogram time series |
| plot-method | Method plot for a matrix of histograms |
| plot-method | plot for a TdistributionH object |
| plot-method | plot for a distributionH object |
| plot-TdistributionH | plot for a TdistributionH object |
| plotPredVsObs | A function for comparing observed vs predicted histograms |
| plot_errors | A function for plotting functions of errors |
-- R --
| register | Method 'register' |
| register-method | Method 'register' |
| registerMH | Method registerMH |
| registerMH-method | Method registerMH |
| RetHTS | A histogram-valued dataset of returns |
| rQQ | Method 'rQQ' |
| rQQ-method | Method 'rQQ' |
-- S --
| set.cell.MatH | Method set.cell.MatH assign a histogram to a cell of a matrix of histograms |
| set.cell.MatH-method | Method set.cell.MatH assign a histogram to a cell of a matrix of histograms |
| ShortestDistance | Shortes distance from a point o a 2d segment |
| show | Method show for distributionH |
| show-MatH | Method show for MatH |
| show-method | Method show for MatH |
| show-method | Method show for distributionH |
| skewH | Method 'skewH': computes the skewness of a distribution |
| skewH-method | Method 'skewH': computes the skewness of a distribution |
| stations_coordinates | Stations coordinates of China_Month and China_Seas datasets |
| stdH | Method 'stdH': computes the standard deviation of a distribution |
| stdH-method | Method 'stdH': computes the standard deviation of a distribution |
| subsetHTS | Method 'subsetHTS': extract a subset of a histogram time series |
| subsetHTS-method | Method 'subsetHTS': extract a subset of a histogram time series |
| summaryHTS | A function for summarize HTS |
-- T --
| TdistributionH | Class TdistributionH |
| TdistributionH-class | Class TdistributionH |
| TMatH | Class TMatH |
| TMatH-class | Class TMatH |
-- W --
| WassSqDistH | Method 'WassSqDistH' |
| WassSqDistH-method | Method 'WassSqDistH' |
| WH.1d.PCA | Principal components analysis of histogram variable based on Wasserstein distance |
| WH.bind | Method WH.bind |
| WH.bind-method | Method WH.bind |
| WH.bind.col | Method WH.bind.col |
| WH.bind.col-method | Method WH.bind.col |
| WH.bind.row | Method WH.bind.row |
| WH.bind.row-method | Method WH.bind.row |
| WH.correlation | Method WH.correlation |
| WH.correlation-method | Method WH.correlation |
| WH.correlation2 | Method WH.correlation2 |
| WH.correlation2-method | Method WH.correlation2 |
| WH.mat.prod | Method WH.mat.prod |
| WH.mat.prod-method | Method WH.mat.prod |
| WH.mat.sum | Method WH.mat.sum |
| WH.mat.sum-method | Method WH.mat.sum |
| WH.MultiplePCA | Principal components analysis of a set of histogram variable based on Wasserstein distance |
| WH.plot_multiple_indivs | Plot histograms of individuals after a Multiple factor analysis of Histogram Variables |
| WH.plot_multiple_Spanish.funs | Plotting Spanish fun plots for Multiple factor analysis of Histogram Variables |
| WH.regression.GOF | Goodness of Fit indices for Multiple regression of histogram variables based on a two component model and L2 Wasserstein distance |
| WH.regression.two.components | Multiple regression analysis for histogram variables based on a two component model and L2 Wasserstein distance |
| WH.regression.two.components.predict | Multiple regression analysis for histogram variables based on a two component model and L2 Wasserstein distance |
| WH.SSQ | Method WH.SSQ |
| WH.SSQ-method | Method WH.SSQ |
| WH.SSQ2 | Method WH.SSQ2 |
| WH.SSQ2-method | Method WH.SSQ2 |
| WH.var.covar | Method WH.var.covar |
| WH.var.covar-method | Method WH.var.covar |
| WH.var.covar2 | Method WH.var.covar2 |
| WH.var.covar2-method | Method WH.var.covar2 |
| WH.vec.mean | Method WH.vec.mean |
| WH.vec.mean-method | Method WH.vec.mean |
| WH.vec.sum | Method WH.vec.sum |
| WH.vec.sum-method | Method WH.vec.sum |
| WH_2d_Adaptive_Kohonen_maps | Batch Kohonen self-organizing 2d maps using adaptive distances for histogram-valued data |
| WH_2d_Kohonen_maps | Batch Kohonen self-organizing 2d maps for histogram-valued data |
| WH_adaptive.kmeans | K-means of a dataset of histogram-valued data using adaptive Wasserstein distances |
| WH_adaptive_fcmeans | Fuzzy c-means with adaptive distances for histogram-valued data |
| WH_fcmeans | Fuzzy c-means of a dataset of histogram-valued data |
| WH_hclust | Hierarchical clustering of histogram data |
| WH_kmeans | K-means of a dataset of histogram-valued data |
| WH_MAT_DIST | L2 Wasserstein distance matrix |
-- misc --
| *-method | Method * |
| *-methods | Method * |
| + | Method + |
| +-method | Method + |
| --method | Method - |
| [ | extract from a MatH Method [ |
| [-method | extract from a MatH Method [ |
| _PACKAGE | Histogram-Valued Data Analysis |