Version 1.0.5
  - "confounded confounders" will now be removed from the status label of a confounded variable.
    - Example: If the association of D with feature Y is confounded by C1 and C2, but the association of C1 with Y is in turn confounded by C2, then only C2 will be listed as confounder of Y~D
  - improvments in some logging messages
  - featureMat is now checked for non-numeric columns at start-up.

Version 1.0.4
  - MetaDeconfound()
    - improved error handling for glms/glmers whith highly collinear metavariables
    - bugfix in confidence interval calculations
    - bugfix for ncol(fatureMat) == 1
    - new optional input argument "mediationMat"
      - perform mediation analysis between features of featureMat and mediadtionMat, controlled for effects from variables in metaMat
      - features in mediationMat will not be tested for confounding effects on each other or variables from metaMat
    - new optinoal argument "mediationMat"
      - supply a second omics space to perform mediation analysis between featureMat and mediationMat
    - new optional argument "adjustLevel"
      - specify different multiple testing p-value correction approaches
        - 1: correct for number of features per metavariable (default)
        - 2: correct for number of features x number of meatavariables
        - 3: correct for number of features x number of mediationMat features (default when mediationMat is supllied)

  - BuildHeatmap()
    - new options "starSize" and "starNudge_y" to control size and positioning of confounding status asterisks/circles
    - metavariables can be split into separate groups by adding a "grounpingVar" columns to metaDeconfOutput.
      - this is done by default when mediationMat is supplied to MetaDeconfound()
      - uses facet_grid(), so output can be tweaked using "+ theme(strip.XXX)" options

  - BuildConfounderMap()
    - new plot generating function
    - for each feature it creates a circos plot of confounder-confounded relations between metavariables associated to this feature.
    - returns a list of ggraph ggplot2 plots
    - ggraph package must be loaded for plotting elements of the returned list.

Version 1.0.3
  - implementation of new funtion GetPartialEfSizes()
    - adds partial R-squared as additional effect size metric
    - new output column "partial": additional variance of a feature explained by
      adding a metavariable to a linear (mixed effects) model containing all
      other metavariables associated to the feature
    - new output column "partialRel": proportion of explainable variance of a feature tracable to a metavariable
      (e.g. partialRel = 0.5 --> metavariable x can explain 50% of all explainable variance of fature y -- all other metavariables significantly associated to feature y can explain the remaining 50% of overall explainable variance of fature y)
    - new output column "PartialNorm": proportion of explained variance of a metavariable set in relation to maximally explainable variance (i.e. variance not explainable by all other metavariables)
  - bug fix of bug disabling computation of confidence intervalls of lrts
  - implementaion of truly linear computation mode when nnodes = 1
  - additional test to filter out non trustworthy lrts in some scenarios with highly colinear metavariables
  - stricter tests for complete separaion when glms are computed

  - BuildHeatmap()
    - added "plotPartial" argument to choose between
      - "marginal": plotting naive/marginal effect sizes based on CLiff's Delta/Spearman's Rho
      - "partial": plotting signed partial R-squared, including an additional column showing the total explainable variance per feature
      - "partialRel": plotting proportion of explainable variance of a feature tracable to a metavariable
      - "partialNorm": plotting respective normalized partial effect size

Version 1.0.2
  - CRAN release

Version 1.0.1
  - fixes for CRAN resubmission

Version 1.0.0
  - CRAN submission

Version 0.3.1
  - reduced side effects after interrupted metadeconfoundR runs (tmp file removal)
  - bugfix: Qcutoff now correctly used to collect potential confounders from naive associations
  - improved initial input data quality control (order, problematic row-/colnames, class)
  - minor bugfixes in Buildheatmap()
  - added LogLevel argument to change verbositiy of logging

Version 0.3.0
  - randomVar argument is now simplified: just supply character vector of metaMat variables to be treated as random effects (e.g. randomVar = c("var1", "var5"))
  - NEW fixedVar argument implemented: same usage as randomVar, but adds fixed effect terms to all models
  - implemented computational speed-up for datasets with large number of metavariables
  - metadeconfoundR is now compatible with Unix AND Windows operation systems
  - fixed a bug that lead to an error when a feature with extremely low number non-NA values was present in featureMat
  - improved error messaging for an error in BuildHeatmap function
  - addition of NEW "rawCount" mode, that runs modelling steps on not normalized/rarefied data by including totalReadCount per sample information into glm/glmer comparisons
    - for naive significance test and effect size calculation, rawCounts will be normalized by dividing by totalReadCount per sample
  - updated implementation of logistic regression mode: test for association of binary features to metavariables by setting "logistic = TRUE"
  - experimental NEW feature: return all computed models alongside normal Metadeconfound() output by setting collectMods = TRUE. WARNING: For now, this only works without utilizing parallel processing in the model building step. Larger datasets might take a lot of time processing.
  - NEW BuildHeatmap() argument "tileBordCol" sets tile border color (Default "black")
  - NEW BuildHeatmap() argument "reOrder" to turn on/of sorting of features and metavariables in the reulsting plot

Version 0.2.8
  - changes in status label names and corresponding plotting behavior
  - reversed default color scheme for effect size plotting (red = low, blue= high)
  - computation of confidence intervals in model building step now default
  (with added OK_d label for deconfounded but doubtful associations)

Version 0.2.7
  - improved error messages and behavior for no/low number of significant associations
    - can now plot results with only a single feature/and or metadata

Version 0.2.6
  - fixed bug in legend creation of BuildHeatmap(cuneiform = TRUE), direction of association now labeled correctly

Version 0.2.5
  - fixed bug in "keepFeature" functionality of BuildHeatmap()
  - fixed bug in BuildHeatmap() that resulted in incomplete removal of features showing not a single significant association
  - implemented speedup for runs of Metadeconfound(startStop = "naiveStop") with large number of metadata

Version 0.2.4
  - Buildheatmap(cuneiform = TRUE) can now handle NA or zero effect sizes (added NEW symbol)

Version 0.2.3
  - fixed bug that only occurred when metadata only has one column
  - implemented speedup when startStop = "naiveStop"

Version 0.2.2
  - added functionality to BuildHeatmap()
    - range of shown effectsizes in using d_range parameter (set to "full" for consistent legend between plots)
    - range of colors used to show effect sizes can be changed using d_col
    - matavariables and features, that should be kept in the plot even without passing q_cutoff and d_cutoff cutoffs, can be supplied using keepMeta and keepFeature parameters

Version 0.2.1
  - added functionality to Metadeconfound()
    - output can now also be generated in long format
  - added functionality to BuildHeatmap()
    - input can now also be read in long format
    - "human readable"" names for features and metadata can be supplied in addition to the Metadeconfound() output and will be plotted instead of "machine readable" names
  - slightly improved help pages

Version 0.2.0
  - NEW function ImportLongPrior() to easily import prior knowledge of feature
  metadata associations present in the current dataset.
  - slightly improved help pages

Version 0.1.9
	-	bugfix concerning random effect variable behavior
	-	minor aesthetic improvements in BuildHeatmap() output

Version 0.1.8
	-	introduction of global parameter "logistic"
		-	logistic = TRUE: analyzing binary features instead of continuous using logistic regression models

Version 0.1.7

	-	bug fix in BuildHeatmap function

Version 0.1.2
    -   critical bug fixed, that resulted in greatly increased "NS" status labeling
