search for: abscontdistribut

...uels I am trying to use the (very nice) package distrMod as I want to implement maximum likelihood (ML) fit of some univariate data for which I have derived a theoretical continuous density (pdf). As it is a parametric density, I guess that I should implement myself a new distribution of class AbscontDistributions (as stated in the pdf on "creating new distributions in distr"), and then use MLEstimator() from the distrMod package. Is that correct or is there a simpler way to go? Note that I want to use the distr package because it allows me to implement simply the convolution of my theoreti...

...for these functions in S). All distributions of the " base" package for which such "r", "d", "p", and "q" functions exist are implemented (essentially by wrappers of the origininal code) as subclasses of either of the two the subclasses "AbscontDistribution" or " DiscreteDistribution". This approach seems very appealing to us from a conceptual viewpoint: Just pass an object of some derived distribution class to a generic function as argument and let the dispatching mechanism decide what to do on run-time. As an example, we ma...

...much/ faster / slimmer for results of *,/,^ + slot d for results of *,/, exp() now is correct at 0 by extrapolation COMFORT METHODS + getLow/getUp methods (for "reasonable" endpoints for distributions with possibly unbounded support) for *DiscreteDistribution, *AbscontDistribution, *UnivarLebDecDistribution, *UnivarMixingDistribution + q.r, p.l (methods for right continuous quantile function and left continuous cdf) for * DiscreteDistribution * AbscontDistribution (takes care of constancy regions in p) * UnivarLebDecDistr...

...much/ faster / slimmer for results of *,/,^ + slot d for results of *,/, exp() now is correct at 0 by extrapolation COMFORT METHODS + getLow/getUp methods (for "reasonable" endpoints for distributions with possibly unbounded support) for *DiscreteDistribution, *AbscontDistribution, *UnivarLebDecDistribution, *UnivarMixingDistribution + q.r, p.l (methods for right continuous quantile function and left continuous cdf) for * DiscreteDistribution * AbscontDistribution (takes care of constancy regions in p) * UnivarLebDecDistr...

Hello, May be somebody can help me... I am trying to find a solution of a convolution equation using fft (and unfortunately I do not have a good background for this). So I am just trying to figure out how it can be implemented in R. I have two multidimensional independent variables X and Z and I know their densities fx and fz, which are multidimensional arrays. So I have to find the density of

Hi, I guess that my problem has an obvious answer, but I have not been able to find it. Suppose I create a custom function, consisting of two beta-distributions: myfunction <- function(x) { dbeta(x,2,6) + dbeta(x,6,2) } How can I calculate the quantiles of myfunction? I have not seen any continous function treated in the docs, and applying the "quantile function" gives me an

...lability on CRAN of a new package distr . It is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "base" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. Additionally, we also provide classes for a standardized treatment...

...tion of "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "base" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. Additionally, we also provide classes for a standardized treatment...

...lability on CRAN of a new package distr . It is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "base" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. Additionally, we also provide classes for a standardized treatment...

...tion of "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "base" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. Additionally, we also provide classes for a standardized treatment...

...******* "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "stats" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. -------------------------------------------------------------------...

...******* "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "stats" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. -------------------------------------------------------------------...

...tion of "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "base" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. Additionally, we also provide classes for a standardized treatment...

...tion of "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "base" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. Additionally, we also provide classes for a standardized treatment...

...******* "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "stats" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. -------------------------------------------------------------------...

...******* "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "stats" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. -------------------------------------------------------------------...

...******* "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "stats" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. -------------------------------------------------------------------...

...******* "distr": "distr" is to provide a conceptual treatment of random variables (r.v.'s) by means of S4--classes. A virtual mother class "Distribution" is introduced. All distributions of the "stats" package are implemented as subclasses of either "AbscontDistribution" or "DiscreteDistribution". Using these classes, we also provide (default) methods to automatically generate the image distributions under unary mathematical operations as well as a general convolution algorithm. -------------------------------------------------------------------...

Is there any R function that computes the convolution of the double exponential distribution? If not, is there a good way to integrate ((q+x)^n)*exp(-2x) over x from 0 to Inf for any value of q and for any positive integer n? I need to perform the integration within a function with q and n as arguments. The function integrate() is giving me this message: "evaluation of function gave a

> On 26 Aug 2017, at 16:39, niharika singhal <niharikasinghal1990 at gmail.com> wrote: > > Hi, > > Thanks for your mail, and time > > It is not working for some arguments, when mean value is like >6. > > > case > > mc0 <- c(0.08844446,0.1744455,0.1379778,0.1209769,0.1573065,0. > 1134463,0.2074027) > > rv