NEWS

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RCM 1.11.3

• For the unconstrained models: fit feature models one by one and Gram-Schmidt orthogonalize and center afterwards, rather than using Lagrange multipliers and huge Jacobian matrices. This will use less memory and speed up computations, but may yield slightly different solutions. Nothing changes for the constrained models.

RCM 1.11.2

• Explicitly import stats::model.matrix, and only load necessary VGAM functions

RCM 1.11.0

• Added FAQ section in vignette with first frequent question on number of samples not shown.
  • Fixed bugs for plots of data with missing values, and added tests.

RCM 1.5.7

• Update vignette to number table of contents

RCM 1.5.6

• Replace deprecated guides( =FALSE) by guides(=“none”)

RCM 1.5.5

• Bug fix for higher dimension residualPlot function, and tests for this function

RCM 1.5.4

• A note in the vignette and in the help file of plot.RCM regarding limited number of combinations of constraining variables. Also a warning is now thrown

RCM 1.5.2

• Avoid returning nulls for residualPlot

RCM 1.2.4

• Adding a new inflVar variable to disambiguate in the influence plotting
  • More argument checking + tests for the plot.RCM function

RCM 1.2.3

• Rename a and b to rowExp and colExp to avoid partial matching
  • Allow rowExp and colExp to be adapted for constrained correspondence analysis starting values as well

RCM 1.2.2

• Moving the online manual information to the vignette

RCM 1.2.1

• Vertical reference line in residual plot
  • Bug fix for problematic variable names

RCM 1.2.0

• Missing values in count matrix are now allowed. They simply do not contribute to the parameter estimation, but the rest of the row (or column) is still used.

RCM 1.0.1

• Bug fix in buildCovMat() to avoid false warning
  • Check for alias structure in confounder and covariate matrices

RCM 1.0.0

• Release on BioConductor

RCM 0.3.0

• Importance parameters (\psi) are enabled again when non-parametric response functions are used, but not used for plotting.
  • 2D sample plots for constrained ordination with non-parametric response functions have been disabled, as they are not interpretable. Variable plots are the only 2D plots still allowed
  • Explained deviance and inertia can be plotted on the axes rahter than the (\psi)’s using the “plotPsi” argument to the plot.RCM() function.
  • Possibility to provide lower dimensional fits has been disabled. RCM is fast enough to fit the whole model.

RCM 0.2.0

• Importance parameters (\psi) are no longer calculated when non-parametric response functions are used.