Package index • RMAWGEN

All functions
`ComprehensivePrecipitationGenerator()`	The comprehensive Precipitation Generator
`ComprehensiveTemperatureGenerator()`	The Comprehensive Temperature Generator
`ElevationOf()`	Extracts the elevation of a meteorological station expressed in meters above a reference (sea level)
`GPCA-class`	GPCA-class
`GPCA()`	This function makes a Gaussianization procedure based on PCA iteration ( see `GPCA_iteration`)
`GPCA_iteration()`	This function makes an iteration of PCA-Gaussianization process
`GPCAiteration-class` `GPCAiteration`	GPCAiteration-class
`GPCAvarest2-class` `GPCAvarest2`	GPCAvarest2-class
`NewVAReventRealization()`	Generates a new realization of a VAR model
`PrecipitationEndDay()`	Gets the last day in a precipitation time series, expressed in decimal julian days since 1970-1-1 00:00 UTC
`PrecipitationStartDay()`	Gets the first day in a precipitation time series, expressed in decimal julian days since 1970-1-1 00:00 UTC
`RMAWGEN-package` `RMAWGEN`	R - Multi-site Autoregressive WEather Generator
`TemperatureEndDay()`	Gets the last day in a temperature time series, expressed as decimal julian days since 1970-1-1 00:00 UTC
`TemperatureStartDay()`	Gets the first day in a temperature time series, expressed as decimal julian days since 1970-1-1 00:00 UTC
`VAR_mod()`	Modified version of `VAR` function allowing to describe white-noise as VAR-(0) model (i. e. `varest` objects)
`WhereIs()`	Gets the toponym where a meteorological station is located
`acvWGEN()`	Plots the auto- and cross- covariance functions between measured and simulated data for several stations
`adddate()`	Inserts three columns (year,month,day) passing dates to a matrix or to a dataframe
`addsuffixes()`	Adds suffixes for daily maximum and minimum temperature to the names of a column data frame
`arch_test()`	`arch.test` function for `varest2` object
`continuity_ratio()`	Calculates the continuity ratio of a set of precipitation measured or generated data in several sites as defined by Wilks, 1998 (see reference link)
`countNAs()`	counts NAs in each row of `data`
`covariance()`	Calculates the covariance matrix of the normally standardized variables obtained from the columns of `x`
`extractTnFromAnomalies()`	Extracts generated time series of Daily Minimum Temperature from a random multi-realization obtained by `generateTemperatureTimeseries` function
`extractTxFromAnomalies()`	Extracts generated time series of Daily Maximum Temperature from a random multi-realization obtained by `generateTemperatureTimeseries` function
`extractdays()`	Extracts the rows of a matrix corresponding to the requested days (expressed as dates YYYY-MM-DD) given the date (origin) of the first row
`extractmonths()`	Extracts the rows of a matrix corresponding to requested months of a year given the date (origin) of the first row
`extractyears()`	Extracts the elements of a data frame corresponding to a period between `year_min` and `year_max` for the stations listed in `station`
`findDate()`	Finds the date corresponding a row index of a matrix given the date (origin) of the first row
`forecastEV()`	Forecasts the expected value of a VAR realization given the prievious one
`forecastResidual()`	Forecasts the residual value of a VAR realization given the white noise covariance matrix
`generateTemperatureTimeseries()`	Returns time series of Daily Maximum and Minimum with a random multi-realization obtained by using `newVARmultieventRealization`. This function is called by `ComprehensiveTemperatureGenerator`.
`getDailyMean()`	Calculates the daily means of a range of days around each date of a data frame corresponding to a period between `year_min` and `year_max` for stations listed in `station`
`getMonthlyMean()`	Calculates the monthly means of a data frame corresponding to a period between `year_min` and `year_max` for stations listed in `station`
`getVARmodel()`	Either creates a VAR model or chooses a VAR model by using VAR or VARselect commands of `vars` package
`inv_GPCA()`	This function makes an inverse Gaussianization procedure besad on PCA iteration ( see `inv_GPCA_iteration`
`inv_GPCA_iteration()`	This function makes an inverse iteration of PCA-Gaussianization process
`is.monthly.climate()`	Verifies if 'climate' represents the monthly climatology in one year, i.e 'climate' is monthly.climate type matrix whose rows represent months and each column represents a station. It is also used in `setComprehensiveTemperatureGeneratorParameters`.
`months_f()`	months REPLACEMANT
`newVARmultieventRealization()`	Generates several realizations of a VAR model
`normality_test()`	`normality.test` method for `varest2` object
`normalizeGaussian()`	Converts a random variable `x` extracted by a population represented by the sample `data` or `sample` to a normally-distributed variable with assigned mean and standard deviation or vice versa in case `inverse` is `TRUE`
`normalizeGaussian_prec()`	Converts precipitation values to "Gaussinized" normally-distributed values taking into account the probability of no precipitation occurrences. values or vice versa in case `inverse` is `TRUE`
`normalizeGaussian_severalstations()`	Converts several samples `x` random variable extracted by populations represented by the columns of `data` respectively or `sample` to a normally-distributed samples with assinged mean and standard deviation or vice versa in case `inverse` is `TRUE`
`normalizeGaussian_severalstations_prec()`	DEPRECATED Converts several samples `x` random variable (daily precipitation values) extracted by populations represented by the columns of `data` respectively or `sample` to a normally-distributed samples with assinged mean and standard deviation or vice versa in case `inverse` is `TRUE` using the function `normalizeGaussian_prec`
`plotDailyClimate()`	Plots daily climatology through one year
`plot_sample()`	It makes a plot by sampling (e.g. monthly) the variables `x` and `y`
`print(<GPCA>)` `print(<GPCAiteration>)`	`print` S3 method for `GPCA` or `GPCA_iteration` object
`qqplot.lagged()`	This function creates a Q-Q plot of the `lag`-lag moving cumulative addition of the values in the samples `x,y,z`
`qqplotTnTxWGEN()`	Makes a qqplot of measured and simulated data for several stations.
`qqplotTnTxWGEN_seasonal()`	Makes four seasonal qqplots (winter, spring, summer and autumn) of measured and simulated data for several stations.
`qqplotWGEN()`	Makes a qqplot and Wilcoxon test between the two columns of `val`
`qqplot_RMAWGEN_Tx()` `qqplot_RMAWGEN_Tn()` `qqplot_RMAWGEN_deltaT()` `qqplot_RMAWGEN_prec()`	It makes the Q-Q plots observed vs generated time series of daily maximum, minimum temperature and daily thermal range for a list of collected stochastic generations
`qqplotprecWGEN()`	Makes a qqplot of measured and simulated data for several stations.
`qqplotprecWGEN_seasonal()`	Makes four seasonal qqplots (winter, spring, summer and autumn) of measured and simulated data for several stations.
`removeNAs()`	Replaces each entry of the rows containing NA values with NA
`rescaling_monthly()`	This function adjusts the monthly mean to a daily weather dataset (e. g. spline-interpolated temperature)
`residuals(<varest2>)`	`residuals` S3 method for `varest2` object
`serial_test()`	`serial.test` function for `varest2` object
`setComprehensiveTemperatureGeneratorParameters()`	Computes climatic and correlation information useful for creating an auto-regeressive random generation of maximum and minimun daily temparature. This function is called by `ComprehensiveTemperatureGenerator`.
`splineInterpolateMonthlytoDaily()`	Interpolates monthly data to daily data using `spline` and preserving monthly mean values
`splineInterpolateMonthlytoDailyforSeveralYears()`	Interpolates monthly data to daily data using `splineInterpolateMonthlytoDaily` for several years
`trentino`	Trentino Dataset
`varest-class` `varest`	varest-class
`varest2-class` `varest2`	varest2-class

Reference

All functions