cbind to non-redundant
Linear rescaling of numeric vertor or matrix
CV of array
Check how multiple groups of data separate or overlap based on mean +/- sd
Add text before file-extension
Express difference as ppm
Organize Data as Separate List-Entries
Append vectors or lists, without duplcating common elements
Connect edges to from tree and extract all possible branches
Adjust Value With Different Decimal Prefixes To Single Prefix Plus Unit
Check for strict (ascencing or descending) order
Check If File Is Available For Reading
Check order of multiple groups including non-overlapping SEM-margins
checkGrpOrder
Replace Most Distant Values by NA
Compare means of two vectors by permutation test
Check length of vector
Reorganize results of search for close (similar) values in matrix-view
Standard error of median for each column by bootstrap
Check for similar values in series
Planing for making all multiplicative combinations
Combine Vectors From List And Return Basic Count Statistics
sd for each column
Transform numeric values to color-gradient
Combine Redundant Lines In List
Combine/reduce redundant lines based on specified column
combineReplFromListToMatr
Combine replicates from list to matrix
combineRedundLinesInListAcRef
Combine Redundant Lines In List, Deprecated
Value Matching With Option For Concatenated Terms
Confidence Interval To Given Alpha
Find and combine points located very close in x/y space
Create factor-like column regrouping data regrouping simultaneaously by two factors
Convert matrix (eg with redundant) row-names to data.frame
Convert vector to numeric
Assign new transparency to given colors
Count from two vectors number of values close within given limits
Count same start- and end- sites of edges (or fragments)
get coordinates of values/points in matrix according to filtering condition
Difference in ppm between numeric values
Add letter to all elements but not last
Characterize individual contribution of single edges in tree-structures
Cut 3-dim array in list of matrixes (or arrays) similar to organizing into clusters
Cut character-vector at multiple sites
Convert matrix of integer to matrix of x-times repeated column-names
Compute matrix of differences for all pairwise combinations of numeric vector
Convert to simple vector (similar to unlist)
Check list of arrays for consistent dimensions of all arrays
Cut numeric vector to n groups (ie convert to factor)
Bring most extreme to center
Check argument names
Calculate ratios for each column to each column of reference-matrix
Summarize along columns of multiple arrays in list
Combine annotation information from list of matrixes
Search character-string and cut either before or after
Compare by distance/difference
Compare 'dat' to confindence interval of linare model 'lMod' (eg from lm())
Check argument for Location of legend
Get all combinations with TRUE from each column
Check Factor
Correct vector to unique
Complete list of arrays for same dimensions
Search (complementing) columns for best coverage of non-NA data for rowNormalization (main)
Compare by PPM
Compare by log-ratio
Summarize along columns of mult arrays in list
Convert numeric matrix to numeric
Convert/standardize names of 'query' to standard names from 'ref'
Model linear regression and optional plot
checkFileNameExtensions
Function for checking file-names.
Filter nodes & edges for extracting networks (main)
This function allows extracting and filtering network-data based on fixed threshold (limInt
) and add sandwich-nodes (nodes inter-connecting initial nodes) out of node-based queries.
Filter 3-dim array of numeric data (main)
Grow tree
Correct mixed slash and backslash in file path
Get series of values after last discontinuity
Convert anything to data.frame
Segment (1-dim vector) 'dat' into clusters
Check regression arguments
find closest neighbour to numeric vector
Get A value for each group of replicates based on comp
Distances beteenw sorted points of 2-columns
Get M value for each group of replicates based on comp
Paste-concatenate all columns of matrix
Return position of 'di' (numeric vector) which is most excentric (distant to 0), starts with NAs as most excentric
Compose sequence of (function-)calls
Merge Multiple Matrices (main)
Avoid duplicating items between 'curNa' and 'newNa' by incrementing digits after 'extPref' (in newNa)
Search character-string and cut either before or after
Filter for size
Scale between 0 and 1 (main)
Extract numbers before separator followed by alphabetic character
fuse 2 instances of 3dim arr as mult cols in 3dim array
Rescale respective to specific group
Get first minimum
Find overlap instances among range of values in lines
Remove all columns where all data are not finite
Cut string to get all variants from given start with min and max length
row group CV (main)
Get A value for each group of replicates
Check if vector may be numeric content
Remove columns indicated by col-number
Search for (empty) columns conaining only entries defined in 'searchFields' and remove such columns
row group mean (main)
Set lowest value to given value
Replace Special Characters
Choose most frequent or middle of sorted vector
Trim character string: keep only text before 'sep'
Scale between min and max value (main)
Cut string to get all variants from given start with min length, depreciated
Add lower caps to character vector
Pie plot for counting results
Extract NA-neighbour values
Automatic choice of colors
Extract number(s) before capital character
Inspect 'matr' and check if 1st line can be used/converted as header
Trim from Left
Trim from right
Main Normalization function
Normalize columns of 2dim matrix to common linear regression fit
Trim from start
Obtain normalization factor (main)
Row-normalization procedure on matrix or data.frame 'dat'
Refine/filter 'dat1' (1dim dataset, eg cluster) with aim of keeping center of data
Extract numeric part of matrix or data.frame
Trim from end
Extract specific text
Extract just one series, ie channel, of list of arrays
Rescale respective to specific group
Exclude extreme values (based on distance to mean)
Calculate residues of (2-dim) linear model 'lMod'-prediction of/for 'dat'
Raise all values close to lowest value
Flexible extraction of columns
Find similar numeric values from two vectors/matrixes
Summarize columns of matrix (or data.frame) 'x' using apply (main)
row group sd (main)
Reorganize array by reducing dimension 'byDim' (similar to stack() for data-frames)
Normalize by adjusting exponent
row group rowSums per group (main)
Select groups within given range
Filter matrix to keep only first of repeated lines
Reduce to first occurance of repeated lines
Filter for unique elements
Fuse annotation matrix to initial matrix
Filter nodes & edges for extracting networks
This function allows extracting and filtering network-data based on fixed threshold (limInt
) and add sandwich-nodes (nodes inter-connecting initial nodes) out of node-based queries.
Find first of repeated elements
Fuse content of list-elements with redundant (duplicated) names
Fuse pairs to generate cluster-names
Make MA-List object
Make non-redundant matrix
Transform (factor) levels into index
Extract last two numeric parts from character vector
Filter three-dimensional array of numeric data
Extract Longest Common Text Out Of Character Vector
Replacements in list
Check regression arguments
Convert numeric vector to matrix
Filter for unique elements
Find repeated elements
Eliminate close (overlapping) points (in x & y space)
Equal character-length number
Find close numeric values between two vectors
Get first of repeated by column
Print matrix-content as plot
Merge selected columns out of 2 matrix or data.frames
Match names to concatenated pairs of names
Transform columns of matrix to list of vectors
Test multiple starting levels for linear regression model, select best and plot
Fit linear regression, return parameters and p-values
Merge Named Vectors
mergeSelCol3
Extended version of merge for multiple objects (even without rownames)
Multiple moderated pair-wise t-tests from limma
Multiple replacement of entire character elements in simple vector, matrix or data.frame
Minimum distance/difference between values
Transform matrix to non-ambiguous matrix (in respect to given column)
Fast na.omit
Run lm on segmented data (from clustering)
Merge Multiple Matrices
Merge Multiple Matrices from List
rbind on lists
Simple Package Download Statistics from CRAN
Convert Pairs of Node-Names to Non-Oriented Propensity Matrix
Extract pair of numeric values from vector or column-names
Order Lines of Matrix According to Reference (Character) Vector
Simple Multi-to-Multi Matching of (Concatenated) Terms
Number of fragments after cut at specific character(s) within size-range
Convert p-values to lfdr
(re)organize data of (3-dim) array as list of replicates
Moderated pair-wise t-test from limma
Filter lines of matrix for max number of NAs
Advanced paste-collapse
Distance of categorical data (Jaccard, Rand and adjusted Rand index)
Contingenty tables for fit of ranking
Convert ulr-name for reading in raw-mode
Organize values into list and sort by names
Filter lines(rows) and/or columns from all suitable elements of list
Html special character conversion
Partial distance matrix (focus on closest)
Filter for unique elements
Partial unlist of lists of lists
Normalize data in various modes
Read batch of csv-files
Batch reading of Tabulated Text-Files
Read Batch of Excel xlsx-Files
Read tabular content of files with variable number of columns
Reorganize matrix according to clustering-output
Replace NAs by low values
Convert ratio to ppm
Calculate all ratios between x and y
matchNamesWithReverseParts
Value Matching with optional reversing of sub-parts of non-matching elements
Count number of non-numeric characters
make numeric vector non-ambiguous (ie unique)
Number of fragments after cut at specific character(s)
Non-redundant lines of matrix
Match All Lines of Matrix To Reference Note
Protect Special Characters
Row group CV
Remove lines of matrix redundant /duplicated for 1st and 2nd column
Remove or rename enumerator tag/name (or remove entire enumerator) from tailing enumerators
Count number of NAs per row and group of columns
Per line and per group sd-values
Reduce table by aggregating smaller groups
SEM for each row
Rescaling according to reference data using linear regression.
Scale data to given minimum and maxiumum
CV of replicate plates (list of matrixes)
Row Normalize
Normal random number generation with close fit to expected mean and sd
rowCVs
Filter for each group of columns for sufficient data as non-NA
Search points forming lines at given slope
System-date (compressed format)
Rename columns
Simple figure showing line from start- to end-sites of edges (or fragments) defined by their start- and end-sites
simpleFragFig
draws figure showing start- and end-sites of edges (or fragments)
Rescaling of multiple data-sets according to reference data using regression
Search and Select Groups of Replicates
rowSums with destinction of groups (of columns, eg groups of replicates)
sd of median for each row by bootstrap
Standardize (scale) data
Standard eror of median by boot-strap
rowMeans with destinction of groups (of columns, eg groups of replicates)
Search duplicated data over multiple columns, ie pairs of data
Estimate mode (most frequent value)
Make single vector gray-gradient
Make a list of common occurances sorted by number of repeats
Summarize columns (as median,mean,min,last or other methods)
Count number of NAs per sub-set of columns
Print matrix-content as plot
(upper) pairwise x,y combinations
t.test on all individual values against all other values
Check for values within range of reference
Locate Sample Index From Index or Name Of Pair-Wise Comparisons in list or MArrayLM-Object
sd for each row (fast execution)
2-factorial limma-style t-test
2-factorial Anova on single line of data
Unify Enumerators
Sort matrix by two categorical and one integer columns
Report number of unique and redundant elements (optional figure)
Locate duplicates in text and make non-redundant
Write (and convert) csv files
Pairwise x,y combinations
Trim redundant text