| .classify.and.predict | Trains a model an performing prediction |
| .collapse | Collapses the elements of a vector based on a list of factors |
| .cols | Get the cols of a matrix based on a factor |
| .compute.jobs | Takes a queue of jobs and calculates them |
| .final.model | Create a model based on the full data. |
| .fuzzy.true | Returns TRUE when *most* of its parameters are true |
| .job.queue | Build a list of necessary computation jobs |
| .rows | Get the rows of a matrix based on a factor |
| .save.model | Saves a model for later prediction of samples. |
| .select | Selects a feature-vector from a dataset |
| .write.compute.external | Appends the result of a job to the corresponding checkpoint |
| absolutize | Makes a relative file path absolute |
| accuracy | Calculates accuracy of classifier output, given confusion matrix. |
| assert | If argument 'is.na' prints a warning and returns 'FALSE' |
| assert.dir | Tries to guarantee the existance of a directory. |
| assert.file | Tries to guarantee the existance of a file. |
| assert.write | Determines if a file or directory is writable. |
| best.model | Find the best scoring model. |
| best.models | Score different models to find the best. |
| centralize | Performs *centralization* normalization |
| changeLabels | Set and/or combine labels |
| classifier | Constructor function for 'edamethod' objects |
| clean.text | Removes all non-alphanumeric characters from a string/array of strings |
| compute.external | Computes the samples for a given job. |
| confParser | Parses configuration files, returning code blocks found in a 'list' of 'expression's. |
| confusion.matrix | Calculates a contigency matrix (also known as a confusion matrix) |
| customBoxplot | Draws a color-coded boxplot |
| customScatterplot | Draws a simple scatter plot of logarithmized data |
| debug | Prints debug message, when debug option set |
| default.var | Returns current value of a variable, otherwise default value |
| dist.matrix | Prints the so-called 'Mega Matrix' |
| divide | Divides various data types into *blocks* for use in parallel processing |
| doc.gen | Automagically generates Rd documentation for functions |
| doMichiels | Wrapper method which uses sampling to attempt to optimize signature stability |
| doORA | Gene selection based on internally computed 'Over-representation analysis (ORA)' results. |
| doPlots | Wrapper function which calls various visualization methods for expression data |
| dopValueplot | Plots distribution of p-values |
| doQuantplot | Quantile plot (plots quantiles for each sample and medians over groups) |
| doVolcanoplot | Draws a volcano plot for expression values (fold changes vs. p-values) |
| edamethod | Constructor function for 'edamethod' objects |
| eval.edamethod | Wrapper for 'eval' for instances of 'edamethod' |
| feature.scores | Calculates scores for a sampled list of indices (=genes) |
| feature.stability.score | Calculates a stability-score for feature-selection |
| featurePlots | Makes Stability-Plots for features. |
| featurePrints | Prints scores along descriptions into a text-file. |
| filt | Filters out data points that follow below or above given cutoffs. |
| foldChanges | Calculates fold changes. |
| getCluster | Fetches a handle to a running (SNOW) cluster |
| globalEval | Evaluates an expression globally |
| goPlots | Makes Stability-Plots for GO-Terms |
| groupData | Returns a subset of the dataset with a given class |
| intersect.list | Multi-dimensional intersection of a list of vectors |
| interval | Returns the middle 'q' percent of a data set |
| job.name | Returns the string name of a job, given job type and current indices |
| job.path | Abstract function for building the path to a job checkpoint file. |
| list.is | A modified 'is' which recursively digs into 'list' objects via an 'lapply' |
| load.model | Returns a saved 'edamodel'. |
| loadData | Loads a RData-File and returns its content. |
| loess.inner | LOESS normalization, parallelized for cluster computing |
| maplot | Makes an MA plot, given two vectors of expression values |
| mean.middle | Calculates mean of the middle 50 percent (between quantiles 1/3) of the data. |
| median.genes | Calculates the median number of spots corresponding to a single gene |
| modprobe | Attempts to load a given library |
| normalization | Wrapper function to call various normalization routines on a given data set. |
| parallel.loess | LOESS normalization, parallelized for cluster computing |
| parameters | Loads all of the configuration files in the 'config' directory |
| pipeline | Predicting an optimal model for classifying gene-expression data based on random sampling |
| plot.job | Plots a histogram for a given classification or FSS job |
| predict.edamodel | Predicts the outcome of new patients using a previously trained model. |
| predictor | Constructor function for 'edamethod' objects |
| print.edamethod | Wrapper for 'print' for instances of 'edamethod' |
| pValues | Computes the p-values for the genes between two sample groups. |
| randomsig | Produces a random gene signature |
| RCI | Calculates Relative Classifier Information. |
| rpart.select | Feature select based on ('rpart') decision trees |
| save.plot | Captures the output of the 'plot' function into a graphics file. |
| select.2way | Selects gene indices, based on minimal p-value and maximum fold change. |
| selector | Constructor function for 'edamethod' objects |
| sink.file | Permanently sinks output to a log file. |
| smad | Scaled Median Absolute Deviation normalization |
| snow.cluster | Identifies whether a SNOW cluster is currently running |
| sources | Sources all of the files under './R/*.R' |
| spots2genes | Combines spots into 'genes'. |
| sum.list | Function for summing the contents of a 'list' |
| svm.select | Feature select based on support vector machines (SVMs) (see 'svm') |
| tupleApply | Apply a function over all possible groupwise combinations of sample groups. |
| tuples | Returns a list of 2-tuples giving all pairwise combinations between 1 and n |
| wrap.eval | Evaluates an expression while trapping its output in a file. |
| zscores | Calculates z-scores for a data matrix |