Revision history for the R/lineup package
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copyright (c) 2011-2021, Karl W Broman

    The R/lineup package is free software; you can redistribute it
    and/or modify it under the terms of the GNU General Public
    License, version 3, as published by the Free Software Foundation.

    This program is distributed in the hope that it will be useful,
    but without any warranty; without even the implied warranty of
    merchantability or fitness for a particular purpose.  See the GNU
    General Public License for more details.

    A copy of the GNU General Public License, version 3, is available
    at https://www.r-project.org/Licenses/GPL-3
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Version 0.40, 2021-11-03

    Fixed lineupversion() to handle a case like "0.40".


Version 0.38-3, 2020-11-16

    Fixed use of class(), for example using inherits(object, "blah")
    rather than "blah" %in% class(object).

    Fixed potential problem in documentation, since plot() has moved
    from the graphics package to base.

    Converted documentation to use markdown


Version 0.37-11, 2019-03-06

    Fixed a bug in distee()

    Fixed test for corbetw2mat(), due to upcoming change in
    sample(), in R 3.6


Version 0.37-10, 2018-03-23

    Add ByteCompile to the DESCRIPTION file


Version 0.37-9, 2017-05-19

    Small changes to avoid Note about "R_registerRoutines"


Version 0.37, 2015-07-20:

    Add a vignette and example data.

    calc.locallod can do calculations in parallel


Version 0.36, 2014-05-02:

    Convert documentation to use roxygen2


Version 0.35, 2012-10-25:

    Only minor changes


Version 0.34, 2012-10-15:

    In plot2dist, make default smoothScatter=FALSE


Version 0.33, 2012-08-14:

    Added function combinedist, for combining distance matrices


Version 0.32, 2012-08-08:

    Added addcovar and intcovar arguments to calc.locallod.


Version 0.31, 2012-03-02:

    Added NAMESPACE.

    Revised lineupversion() to use packageVersion().


Version 0.30, 2011-05-11:

    This is a new package to contain tools for lining up two data
    matrices, to detect and correct sample mix-ups.  The key examples
    are gene expression microarray data on a large set of samples on
    two tissues, or between genotype and gene expression.

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end of ChangeLog
