@@ -13,9 +13,7 @@ analyzes its variance with the bootstrap method. The \code{data} and

 the number of trees \code{K} have to be specified. 

 }

 \usage{

-bootstrap(data, K, no.start.sol = 100, eps = 0, weighing = FALSE, 

-          equal.edgeweights = TRUE, seed = (-1), B = 1000, 

-          conf.interval = 0.05)

+bootstrap(data, K, \dots)

 }

 \arguments{

@@ -23,24 +21,26 @@ bootstrap(data, K, no.start.sol = 100, eps = 0, weighing = FALSE,

     learning the trees mixture model. }

   \item{K}{An \code{integer} larger than 0 specifying the number of

     branchings in the mixture model.} 

-  \item{no.start.sol}{An \code{integer} larger than 0 specifying the number of starting solutions for the k-means

-    algorithm. The default value is 100.}

-  \item{eps}{A \code{numeric} giving the minimum conditional probability to include edge. The

-    default value is 0.}

-  \item{weighing}{A \code{logical} specifying whether to use special

-      weights log(Pr(v)) for the edges (root, v). The default value is \code{FALSE}.}

-  \item{equal.edgeweights}{A \code{logical} specifying whether to use

+  \item{\dots}{  

+    \code{no.start.sol} is an \code{integer} larger than 0 specifying the number of starting solutions for the k-means

+    algorithm. The default value is 100.

+    \code{eps} is a \code{numeric} giving the minimum conditional probability to include edge. The

+    default value is 0.

+    \code{weighing} is a \code{logical} specifying whether to use special

+      weights log(Pr(v)) for the edges (root, v). The default value is \code{FALSE}.

+    \code{equal.edgeweights} is a \code{logical} specifying whether to use

       equal edge weights in the noise component. The default value is

       \code{TRUE}. When you have few data samples always use its default value 

       (\code{TRUE}) to ensure nonzero probabilities for all possible 

-      patterns (sets of events).}

-  \item{seed}{A positive \code{integer} specifying the random generator

+      patterns (sets of events).

+    \code{seed} is a positive \code{integer} specifying the random generator

       seed. The default value is (-1) and then the time is used as a

-      random generator.}

-  \item{B}{An \code{integer} larger than 0 specifying the number of

-      bootstrap samples. Its default value is 1000.}

-  \item{conf.interval}{A \code{numeric} specifying the Confidence level

-      for the intervals. Its default value is 0.05.}

+      random generator.

+    \code{B} is an \code{integer} larger than 0 specifying the number of

+      bootstrap samples. Its default value is 1000.

+    \code{conf.interval} is a \code{numeric} specifying the Confidence level

+      for the intervals. Its default value is 0.05.

+  }

 }

 \value{

@@ -18,8 +18,7 @@

 }

 \usage{

-confIntGPS(data, K, sampling.mode = "exponential", sampling.param = 1, 

-           no.sim = 10000, B = 1000, equal.star = TRUE)

+confIntGPS(data, K, \dots)

 }

 \arguments{

@@ -29,23 +28,25 @@ confIntGPS(data, K, sampling.mode = "exponential", sampling.param = 1,

     of genetic events should NOT be greater than 20.}

   \item{K}{An \code{integer} larger than 0 specifying the number of

     branchings in the mixture model.}

-  \item{sampling.mode}{A \code{character} that specifies the

+  \item{...}{

+    \code{sampling.mode} is a \code{character} that specifies the

       sampling mode ("constant" or "exponential") used in the waiting time

-      simulations. Its default value is "exponential".}

-  \item{sampling.param}{A \code{numeric} that specifies the

+      simulations. Its default value is "exponential".

+    \code{sampling.param} is a \code{numeric} that specifies the

       sampling parameter corresponding to the sampling mode given by

-      \code{sampling.mode}. Its default value is 1.}

-  \item{no.sim}{An \code{integer} larger than 0 giving the number of

+      \code{sampling.mode}. Its default value is 1.

+    \code{no.sim} is an \code{integer} larger than 0 giving the number of

       iterations for the waiting time simulation. Its default values is

-      10000.}

-  \item{B}{An \code{integer} larger than 0 specifying the number of

+      10000.

+    \code{B} is an \code{integer} larger than 0 specifying the number of

       bootstrap samples used in the bootstrap analysis. Its default value

-      is 1000.}

-  \item{equal.star}{A \code{logical} specifying whether to use

+      is 1000.

+    \code{equal.star} is a \code{logical} specifying whether to use

       equal edge weights in the noise component. The default value is

       \code{TRUE}. When you have few data samples always use its default value 

       (\code{TRUE}) to ensure nonzero probabilities for all possible 

-      patterns (sets of events).}

+      patterns (sets of events).

+  }

 }

 \value{

@@ -63,7 +64,7 @@ confIntGPS(data, K, sampling.mode = "exponential", sampling.param = 1,

     confidence intervals are to be calculated should not have more 

     than 20 genetic events. The reason for this is that the number of all possible patterns 

     for which the GPS values are calculated during a computationally intensive simulations 

-    is in this case $2^{20}$. This demands too much memory.

+    is in this case $2^20$. This demands too much memory.

     The GPS examples are time consuming. They are commented out because of the time restrictions of the check of the package.

     For trying out the code please copy it and uncomment it.

 }

@@ -19,12 +19,7 @@

  events in the \code{model} cannot exceed 30.

 }

 \usage{

-distribution(model, ...)

-

-\method{distribution}{model}(model)

-

-\method{distribution}{model, sampling.mode, sampling.param, output.param}(model, 

-                                    sampling.mode, sampling.param, output.param)

+distribution(model, sampling.mode, sampling.param, output.param)

 }

 \arguments{

   \item{model}{An \code{RtreemixModel} object that encodes a

@@ -37,7 +32,7 @@ distribution(model, ...)

     corresponding to the sampling mode given by \code{sampling.mode}.}

   \item{output.param}{A \code{numeric} that specifies the

     sampling parameter for the observed output probabilities

-    corresponding to the sampling mode given by \code{sampling.mode}.}  

+    corresponding to the sampling mode given by \code{sampling.mode}.}    

 }

 \value{

@@ -24,8 +24,7 @@

 }

 \usage{

-fit(data, K, no.start.sol = 100, eps = 0.01, weighing = FALSE, 

-    equal.edgeweights = TRUE, seed = (-1), noise = TRUE)

+fit(data, K, \dots)

 }

 \arguments{

@@ -33,23 +32,25 @@ fit(data, K, no.start.sol = 100, eps = 0.01, weighing = FALSE,

     learning the trees mixture model.}

   \item{K}{An \code{integer} larger than 0 specifying the number of

     branchings in the mixture model.}

-  \item{no.start.sol}{An \code{integer} larger than 0 specifying the number of starting solutions for the k-means

-    algorithm. The default value is 100.}

-  \item{eps}{A \code{numeric} giving the minimum conditional probability to include edge. The

-    default value is 0.01.}

-  \item{weighing}{A \code{logical} specifying whether to use special

-    weights log(Pr(v)) for the edges (root, v). The default value is \code{FALSE}.}

-  \item{equal.edgeweights}{A \code{logical} specifying whether to use

+  \item{\dots}{

+    \code{no.start.sol} is an \code{integer} larger than 0 specifying the number of starting solutions for the k-means

+    algorithm. The default value is 100.

+    \code{eps} is a \code{numeric} giving the minimum conditional probability to include edge. The

+    default value is 0.01.

+    \code{weighing} is a \code{logical} specifying whether to use special

+    weights log(Pr(v)) for the edges (root, v). The default value is \code{FALSE}.

+    \code{equal.edgeweights} is a \code{logical} specifying whether to use

     equal edge weights in the noise component. The default value is

     \code{TRUE}. When you have few data samples always use its default value (\code{TRUE}) 

-    to ensure nonzero probabilities for all possible patterns (sets of events).}

-  \item{seed}{A positive \code{integer} specifying the random generator

+    to ensure nonzero probabilities for all possible patterns (sets of events).

+    \code{seed} is a positive \code{integer} specifying the random generator

     seed. The default value is (-1) and then the time is used as a

-    random generator.}

-  \item{noise}{A \code{logical} indicating the presence of a noise

+    random generator.

+    \code{noise} is a \code{logical} indicating the presence of a noise

     (star) component in the fitted mixture model. It is mostly relevant

     for models with a single tree component, since it is assumed that mixture models with

-    at least two components always have the noise as a first component.}

+    at least two components always have the noise as a first component.

+  }

 }

 \value{

@@ -14,28 +14,29 @@

   have to be specified.

 }

 \usage{

-generate(K, no.events, noise.tree = TRUE, equal.edgeweights = TRUE, 

-         prob = c(0, 1), seed = (-1))

+generate(K, no.events, \dots)

 }

 \arguments{

   \item{K}{An \code{integer} larger than 0 specifying the number of

     branchings in the mixture model.}

   \item{no.events}{An \code{integer} larger than 0 specifying the number of

     genetic events in the mixture model.}

-  \item{noise.tree}{A \code{logical} indicating the presence of a noise

+  \item{\dots}{

+    \code{noise.tree} is a \code{logical} indicating the presence of a noise

     (star) component in the random mixture model. The default value is

-    \code{TRUE}.}

-  \item{equal.edgeweights}{A \code{logical} specifying whether to use

+    \code{TRUE}.

+    \code{equal.edgeweights} is a \code{logical} specifying whether to use

     equal edge weights in the noise component. The default value is

-    \code{TRUE}.}

-  \item{prob}{A \code{numeric} vector of length 2 specifying the

+    \code{TRUE}.

+    \code{prob} is a \code{numeric} vector of length 2 specifying the

     boundaries for the edge weights of the randomly generated trees. The

     first component of the vector (the lower boundary) must be smaller

     than the second component (the upper boundary). The default value

-    is (0.0, 1.0).}

-  \item{seed}{A positive \code{integer} specifying the random generator

+    is (0.0, 1.0).

+    \code{seed} is a positive \code{integer} specifying the random generator

     seed. The default value is (-1) and then the time is used as a

-    random generator.}

+    random generator.

+  }

 }

 \value{

   The method returns an \code{RtreemixModel} object that represents the

@@ -22,13 +22,7 @@

 }

 \usage{

-gps(model, ...)

-

-\method{gps}{model, data}(model, data, sampling.mode = "exponential",

-                         sampling.param = 1, no.sim = 10, seed = (-1))

-

-\method{gps}{model}(model, sampling.mode = "exponential",

-                    sampling.param = 1, no.sim = 10, seed = (-1))                         

+gps(model, data, \dots)                   

 }

 \section{Methods}{\describe{

@@ -48,17 +42,19 @@ gps(model, ...)

     containing the samples (patterns of genetic events) for which the GPS values

     are to be calculated. The length of each of them has to be equal

     to the number of genetic events in the \code{model}.}

-  \item{sampling.mode}{A \code{character} that specifies the

+  \item{\dots}{ 

+    \code{sampling.mode} is a \code{character} that specifies the

     sampling mode ("constant" or "exponential") used in the waiting time

-    simulations. Its default value is "exponential".}

-  \item{sampling.param}{A \code{numeric} that specifies the

+    simulations. Its default value is "exponential".

+    \code{sampling.param} is a \code{numeric} that specifies the

     sampling parameter corresponding to the sampling mode given by

-    \code{sampling.mode}. Its default value is 1.}

-  \item{no.sim}{An \code{integer} larger than 0 giving the number of

-    iterations for the waiting time simulations. Its default value is 10.}

-  \item{seed}{A positive \code{integer} specifying the random generator

+    \code{sampling.mode}. Its default value is 1.

+    \code{no.sim} is an \code{integer} larger than 0 giving the number of

+    iterations for the waiting time simulations. Its default value is 10.

+    \code{seed} is a positive \code{integer} specifying the random generator

     seed. Its default value is (-1) and then the time is used as a

-    random generator.}

+    random generator.

+  }

 }

 \value{

@@ -76,7 +72,7 @@ gps(model, ...)

     The mixture model used for deriving the GPS values should not have more than 

     20 genetic events. The reason for this is that the number of all possible patterns 

     for which the GPS values are calculated during a computationally intensive simulations 

-    is in this case $2^{20}$. This demands too much memory.

+    is in this case $2^20$. This demands too much memory.

     The GPS examples are time consuming. They are commented out because of the time restrictions of the check of the package.

     For trying out the code please copy it and uncomment it.

 } 

@@ -19,14 +19,17 @@

 }

 \usage{

-plot(x, k, fontSize = 8)

+plot(x, y, \dots)

 }

 \arguments{

   \item{x}{An \code{RtreemixModel} object giving the mixture model that should be visualized.}

-  \item{fontSize}{The size of the text labels of the nodes and the edges of the tree components. The default value is 8.}

-  \item{k}{A \code{numeric} giving the specific tree component from the given mixture model that should be plotted. Its value 

-    can be from one to the number of tree components in the given model.}

+  \item{y}{Not specified.}

+  \item{\dots}{

+    \code{fontSize} is the size of the text labels of the nodes and the edges of the tree components. The default value is 8.

+    \code{k} is a \code{numeric} giving the specific tree component from the given mixture model that should be plotted. Its value 

+    can be from one to the number of tree components in the given model.

+  }

 }

 \value{

   The method returns a plot of the mixture model model.

@@ -14,13 +14,7 @@

   patterns together with their waiting and sampling times from the respective model.

 }

 \usage{

-sim(model, ...)

-

-\method{sim}{model}(model, no.draws = 10, seed = (-1))

-

-\method{sim}{model, sampling.mode, sampling.param}(model, sampling.mode, 

-                                                   sampling.param, no.sim = 10,

-                                                   seed = (-1))

+sim(model, sampling.mode, sampling.param, \dots)

 }

 \arguments{

@@ -32,14 +26,16 @@ sim(model, ...)

   \item{sampling.param}{A \code{numeric} that specifies the

     sampling parameter corresponding to the sampling mode given by

     \code{sampling.mode}.}

-  \item{no.draws}{An \code{integer} larger than zero specifying the

-    number of patterns that should be drawn from the given mixture model.}

-  \item{no.sim}{An \code{integer} larger than 0 giving the number of

+  \item{\dots}{

+    \code{no.draws} is an \code{integer} larger than zero specifying the

+    number of patterns that should be drawn from the given mixture model.

+    \code{no.sim} is an \code{integer} larger than 0 giving the number of

     iterations for the waiting time simulations. Its default value is

-    10.}

-  \item{seed}{A positive \code{integer} specifying the random generator

+    10.

+    \code{seed} is a positive \code{integer} specifying the random generator

     seed. Its default value is (-1) and then the time is used as a

-    random generator.}

+    random generator.

+  }

 }

 \value{