The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
runtime error: null pointer passed as argument 1, which is declared to never be null
bug introduced in v1.4.0 that was detected by the
UndefinedBehaviorSanitizer (UBSan) running on CRAN.rowSums2() is now significantly faster for larger
matrices.None of the error messages use a trailing period.
Addressing changes in the C API of R-devel resulted in compiler
errors such as
error: implicit declaration of function 'Calloc'; did you mean 'calloc'? [-Wimplicit-function-declaration].
Addressing changes in stricter compiler flags of R-devel resulted
in compiler warning
embedding a directive within macro arguments has undefined behavior [-Wembedded-directive].
colRanks() and rowRanks() without
explicitly specifying argument ties.method is deprecated
since version 1.3.0. If not explicitly specified, a deprecation warning
is now produced every 25:th call not specifying the
ties.method argument.validateIndices() has been removed. It had been defunct
since version 0.63.0 (2022-11-14).Calling colRanks() and rowRanks()
without explicitly specifying argument ties.method will be
deprecated when using R (>= 4.4.0). The reason is that the current
default is ties.method = "max", but we want to change
that to ties.method = "average" to align it with
base::rank(). In order to minimize the risk for sudden
changes in results, we ask everyone to explicitly specify their intent.
The first notice will be through deprecation warnings, which will only
occur every 50:th call to keep the noise level down. We will make it
more noisy in future releases, and eventually also escalated to defunct
errors.
Using a scalar value for argument center of
colSds(), rowSds(), colVars(),
rowVars(), colMads(), rowMads(),
colWeightedMads(), and rowWeightedMads() is
now defunct.
useNames = NA is defunct.useNames = NA is defunct in R (>= 4.4.0). Remains
deprecated in R (< 4.4.0) for now.useNames = NA,
suggested using useNames = TRUE twice instead of also
useNames = FALSE.useNames = TRUE is the new default for all
functions. For backward compatibility, it used to be
useNames = NA.
colQuantiles() and rowQuantiles()
gained argument digits, just like
stats::quantile() gained that argument in R 4.1.0.
colQuantiles() and rowQuantiles() only
sets quantile percentage names when useNames = TRUE, to
align with how argument names of
stats::quantile() works in base R.
colMeans2() and rowMeans2() gained
argument refine. If refine = TRUE, then the
sample average for numeric matrices are calculated using a two-pass
scan, resulting in higher precision. The default is
refine = TRUE to align it with colMeans(), but
also mean2() in this package. If the higher precision is
not needed, using refine = FALSE will be almost twice as
fast.
colSds(), rowSds(),
colVars(), and rowVars() gained argument
refine. If refine = TRUE, then the sample
average for numeric matrices are calculated using a two-pass scan,
resulting in higher precision for the estimate of the center and
therefore also the variance.
Unnecessary checks for missing indices are eliminated, yielding better performance. This change does not affect user-facing API.
Made colQuantiles() and rowQuantiles()
a bit faster for type != 7L, by making sure percentage
names are only generated once, instead of once per column or
row.
Contrary to other functions in the package, and how it works in
base R, functions colCumsums(), colCumprods(),
colCummins(), colCummaxs(),
colRanges(), colRanks(), and
colDiffs(), plus the corresponding row-based versions, did
not drop the names attribute when both row and column names
were NULL. Now also these functions behaves the same as the
case when neither row or column names are set.
colQuantiles() and rowQuantiles() did
not generate quantile percentage names exactly the same way as
stats::quantile(), which would reveal itself for certain
combinations of probs and digits.
useNames = NA is now deprecated. Use
useNames = TRUE or useNames = FALSE
instead.Package compiles again with older compilers not supporting the C99 standard (e.g. GCC 4.8.5 (2015), which is the default on RHEL / CentOS 7.9). This was the case also for matrixStats (<= 0.54.0).
Added more information to the error message produced when
argument center for col- and
rowVars() holds an invalid value.
Fix two compilation warnings on
a function declaration without a prototype is deprecated in all versions of C [-Wstrict-prototypes].
validateIndices() is now defunct and will eventually be
removed from the package API.colCummins(), colCummaxs(),
rowCummins(), and rowCummaxs() now support
also logical input.DBL_MAX
instead of legacy S constant DOUBLE_XMAX, which is planned
to be unsupported in R (>= 4.2.0).which for colOrderStats()
and rowOrderStats() is out of range, the error message now
reports on the value of which. Similarly, when argument
probs for colQuantiles() and
rowQuantiles() is out of range, the error message reports
on its value too.validateIndices() is deprecated and will eventually be
removed from the package API.Handling of the useNames argument is now done in the
native code.
Passing idxs, rows, and
cols arguments of type integer is now less efficient than
it used to, because the new code re-design (see below) requires an
internal allocation of an equally long R_xlen_t vector that
is populated by indices coerced from R_len_t to
R_xlen_t integers.
R CMD check would produce a NOTE on the package
installation size being large, which no longer is the case. The downside
is that extra overhead when passing integer indices (see above
comment).useNames = NA in the previous release,
colQuantiles() and rowQuantiles() got
useNames = TRUE.useNames = TRUE. To drop them, set
useNames = FALSE. To preserve the current, inconsistent
behavior, set useNames = NA, which, for backward
compatibility reasons, remains the default for now.meanOver() and sumOver(), and
argument method from weightedVar(), that have
been defunct since January 2018.colVars() and rowVars() with argument
center now calculates the sample variance using the
n/(n-1)*avg((x-center)^2) formula rather than the
n/(n-1)*(avg(x^2)-center^2) formula that was used in the
past. Both give the same result when center is the correct
sample mean estimate. The main reason for this change is that, if an
incorrect center is provided, in contrast to the old
approach, the new approach is guaranteed to give at least non-negative
results, despite being incorrect. BACKWARD COMPATIBILITY: Out of all 314
reverse dependencies on CRAN and Bioconductor, only four called these
functions with argument center. All of them pass their
package checks also after this update. To further protect against a
negative impact in existing user scripts, colVars() and
rowVars() will calculate both versions and assert that the
result is the same. If not, an informative error is produced. To limit
the performance impact, this validation is run only once every 50:th
call, a frequency that can be controlled by R option
matrixStats.vars.formula.freq. Setting it to 0 or NULL will
disable the validation. The default can also be controlled by
environment variable R_MATRIXSTATS_VARS_FORMULA_FREQ. This
validation framework will be removed in a future version of the package
after it has been established that this change has no negative
impact.Now colWeightedMads() and
rowWeightedMads() accept center of the same
length as the number of columns and rows, respectively.
colAvgsPerRowSet() and
rowAvgsPerRowSet() gained argument
na.rm.
Now weightedMean() and weightedMedian()
and the corresponding row- and column-based functions accept logical
x, where FALSE is treated as integer 0 and TRUE as
1.
Now x_OP_y() and t_tx_OP_y() accept
logical x and y, where FALSE is treated as
integer 0 and TRUE as 1.
colQuantiles() and rowQuantiles() on a
logical matrix should return a numeric vector for type = 7.
However, when there were only missing values (= NA) in the matrix, then
it would return a “logical” vector instead.
colAvgsPerRowSet() on a single-column matrix would
produce an error on non-matching dimensions. Analogously, for
rowAvgsPerRowSet() and single- row matrices.
colVars(x) and rowVars(x) with
x being an array would give the wrong value if both
argument dim. and center would be
specified.
The documentation was unclear on what the center
argument should be. They would not detect when an incorrect
specification was used, notably when the length of center
did not match the matrix dimensions. Now these functions give an
informative error message when center is of the incorrect
length.
center of
colSds(), rowSds(), colVars(),
rowVars(), colMads(), rowMads(),
colWeightedMads(), and rowWeightedMads() is
now deprecated.colCumprods() and rowCumprods() now
support also logical input. Thanks to Constantin Ahlmann-Eltze at EMBL
Heidelberg for the patch.colCollapse() and rowCollapse() did not
expand idxs argument before subsetting by cols
and rows, respectively. Thanks to Constantin Ahlmann-Eltze
for reporting on this.
colAnys(), rowAnys(),
anyValue(), colAlls(), rowAlls(),
and allValue() with value=FALSE and
numeric input would incorrectly consider all values different
from one as FALSE. Now it is only values that are zero that are
considered FALSE. Thanks to Constantin Ahlmann-Eltze for the bug
fix.
colQuantiles() and rowQuantiles() now
supports only integer, numeric and logical input. Previously, it was
also possible to pass, for instance, character input, but
that was a mistake. The restriction on input allows for further
optimization of these functions.
The returned type of colQuantiles() and
rowQuantiles() is now the same as for
stats::quantile(), which depends on argument
type.
colQuantiles() and rowQuantiles() with the
default type = 7L and when there are no missing values are
now significantly faster and use significantly fewer memory
allocations.colDiffs() and rowDiffs() gave an error
if argument dim. was of type numeric rather than type
integer.
varDiff(), sdDiff(),
madDiff(), iqrDiff(), and the corresponding
row- and column functions silently treated a diff less than
zero as diff = 0. Now an error is produced.
Error messages on argument dim. referred to
non-existing argument dim.
Error messages on negative values in argument dim.
reported a garbage value instead of the negative value.
The Markdown reports produced by the internal benchmark report generator did not add a line between tables and the following text (a figure caption) causing the following text to be included in a cell on an extra row in the table (at least when rendered on GitHub Wiki pages).
weightedVar(), weightedSd(),
weightedMad(), and their row- and column- specific counter
parts now return a missing value if there are missing values in any of
the weights w after possibly dropping (x,
w) elements with missing values in x
(na.rm = TRUE). Previously, na.rm = TRUE
would also drop (x, w) elements where
w was missing. With this change, we now have that for all
functions in this package, na.rm = TRUE never applies to
weights - only x values.colRanks() and rowRanks() now supports
the same set of ties.method as base::rank()
plus "dense" as defined by
data.table::frank(). For backward compatible reasons, the
default ties.method remains the same as in previous
versions. Thank to Brian Montgomery for contributing this.
colCumsums() and rowCumsums() now
support also logical input.
weightedVar(), weightedSd(),
weightedMad(), and their row- and column- specific counter
parts would produce an error instead of returning a missing value when
one of the weights is a missing value.indexByRow(x) where x is a matrix
is now defunct. Use indexByRow(dim(x)) instead.stopifnot() for internal
validation, because it comes with a great overhead. This was only used
in weightedMad(), col-, and
rowWeightedMads(), as well as col- and
rowAvgsPerColSet().Despite being an unlikely use case,
colLogSumExps(lx) / rowLogSumExps(lx) now also
accepts integer lx values.
The error produced when using indexByRow(dim) with
prod(dim) >= 2^31 would report garbage dimensions
instead of dim.
indexByRow(x), where x is a
matrix, is deprecated. Use indexByRow(dim(x)) instead.col-/rowSds() explicitly replicate all
arguments that are passed to
col-/rowVars().weightedMedian(x, interpolate = TRUE) works.colLogSumExps(lx, cols) /
rowLogSumExps(lx, rows) gave an error if lx
has rownames / colnames.
col-/rowQuantiles() would lose rownames
of output in certain cases.
Functions sum2(x) and means2(x) now
accept also logical input x, which corresponds to using
as.integer(x) but without the need for neither coercion nor
internal extra copies. With sum2(x, mode = "double") it
is possible to count number of TRUE elements beyond 2^31-1, which
base::sum() does not support.
Functions col-/rowSums2() and
col-/rowMeans2() now accept also logical input
x.
Function binMeans(y, x, bx) now accepts logical
y, which corresponds to to using
as.integer(y), but without the need for coercion to
integer.
Functions col-/rowTabulates(x) now
support logical input x.
Now count() can count beyond 2^31-1.
allocVector() can now allocate long vectors (longer
than 2^31-1).
Now sum2(x, mode = "integer") generates a warning if
typeof(x) == "double" asking if
as.integer(sum2(x)) was intended.
Inspired by Hmisc::wtd.var(), when
sum(w) <= 1, weightedVar(x, w) now
produces an informative warning that the estimate is invalid.
colAvgsPerColSet() with that of
rowAvgsPerColSet().col-/rowLogSumExp() could core dump R
for “large” number of columns/rows. Thanks Brandon Stewart at Princeton
University for reporting on this.
count() beyond 2^31-1 would return invalid
results.
Functions col-/rowTabulates(x) did not
count missing values.
indexByRow(dim, idxs) would give nonsense results if
idxs had indices greater than prod(dim) or
non-positive indices; now it gives an error.
indexByRow(dim) would give nonsense results when
prod(dim) >= 2^31; now it gives an informative
error.
col-/rowAvgsPerColSet() would return
vector rather than matrix if nrow(X) <= 1. Thanks to
Peter Hickey (Johns Hopkins University) for troubleshooting and
providing a fix.
Previously deprecated meanOver() and
sumOver() are defunct. Use mean2() and
sum2() instead.
Previously deprecated
weightedVar(x, w, method = "0.14.2") is defunct.
Dropped previously defunct
weightedMedian(..., ties = "both").
Dropped previously defunct argument centers for
col-/rowMads(). Use center
instead.
Dropped previously defunct argument flavor of
colRanks() and rowRanks().
rowVars(..., method = "0.14.2") that was added for very
unlikely needs of backward compatibility of an invalid degree-of-freedom
term is deprecated.matrixStats:::benchmark() tried to
run even if not all suggested packages were available.Since anyNA() is a built-in function since R (>=
3.1.0), please use that instead of anyMissing() part of
this package. The latter will eventually be deprecated. For consistency
with the anyNA() name, colAnyNAs() and
rowAnyNAs() are now also available replacing the
identically colAnyMissings() and
rowAnyMissings() functions, which will also be deprecated
in a future release.
meanOver() was renamed to mean2() and
sumOver() was renamed to sum2().
Added colSums2() and rowSums2() which
work like colSums() and rowSums() of the
base package but also supports efficient subsetting via
optional arguments rows and cols.
Added colMeans2() and rowMeans2() which
work like colMeans() and rowMeans() of the
base package but also supports efficient subsetting via
optional arguments rows and cols.
Functions colDiffs() and rowDiffs()
gained argument dim..
Functions colWeightedMads() and
rowWeightedMads() gained arguments constant
and center. The current implementation only support scalars
for these arguments, which means that the same values are applied to all
columns and rows, respectively. In previous version a hard-to-understand
error would be produced if center was of length greater
than one; now an more informative error message is given.
Package is now silent when loaded; it no longer displays a startup message.
Continuous-integration testing is now also done on macOS, in addition to Linux and Windows.
ROBUSTNESS: Package now registers the native API using also
R_useDynamicSymbols().
Cleaned up native low-level API and renamed native source code files to make it easier to navigate the native API.
Now using roxygen2 for help and NAMESPACE (was
R.oo::Rdoc).
rowAnys(x) on numeric matrices x would
return rowAnys(x == 1) and not
rowAnys(x != 0). Same for colAnys(),
rowAlls(), and colAlls(). Thanks Richard
Cotton for reporting on this.
sumOver(x) and meanOver(x) would
incorrectly return -Inf or +Inf if the intermediate sum would have that
value, even if one of the following elements would turn the intermediate
sum into NaN or NA, e.g. with x as
c(-Inf, NaN), c(-Inf, +Inf), or
c(+Inf, NA).
WORKAROUND: Benchmark reports generated by
matrixStats:::benchmark() would use any custom R prompt
that is currently set in the R session, which may not render very well.
Now it forces the prompt to be the built-in "> "
one.
The package API is only intended for matrices and vectors of type
numeric, integer and logical. However, a few functions would still
return if called with a data.frame. This was never intended to work and
is now an error. Specifically, functions colAlls(),
colAnys(), colProds(),
colQuantiles(), colIQRs(),
colWeightedMeans(), colWeightedMedians(), and
colCollapse() now produce warnings if called with a
data.frame. Same for the corresponding row- functions. The use of a
`data.frame will be produce an error in future releases.
meanOver() and sumOver() are deprecated
because they were renamed to mean2() and
sum2(), respectively.
Previously deprecated (and ignored) argument flavor
of colRanks() and rowRanks() is now
defunct.
Previously deprecated support for passing non-vector, non-matrix
objects to rowAlls(), rowAnys(),
rowCollapse(), and the corresponding column-based versions
are now defunct. Likewise, rowProds(),
rowQuantiles(), rowWeightedMeans(),
rowWeightedMedians(), and the corresponding column-based
versions are also defunct. The rationale for this is to tighten up the
identity of the matrixStats package and what types of
input it accepts. This will also help optimize the code
further.
SPEEDUP / CLEANUP: rowMedians() and
colMedians() are now plain functions. They were previously
S4 methods (due to a Bioconductor legacy). The package no longer imports
the methods package.
SPEEDUP: Now native API is formally registered allowing for faster lookup of routines from R.
Package now installs on R (>= 2.12.0) as claimed. Thanks to Mikko Korpela at Aalto University School of Science, Finland, for troubleshooting and providing a fix.
logSumExp(c(-Inf, -Inf, ...)) would return NaN
rather than -Inf. Thanks to Jason Xu (University of
Washington) for reporting and Brennan Vincent for troubleshooting and
contributing a fix.
memcall(src, dest, 0) call when dest == null.
Thanks to Brian Ripley and the CRAN check tools for catching this. We
could reproduce this with gcc 5.1.1 but not with gcc 4.9.2.idxs,
rows and cols were added to all functions such
that the calculations are performed on the requested subset while
avoiding creating a subsetted copy,
i.e. rowVars(x, cols = 4:6) is a much faster and more
memory efficient version than rowVars(x[, 4:6]) and even
yet more efficient than apply(x, MARGIN = 1L, FUN = var).
These features were added by Dongcan Jiang, Peking University, with
support from the Google Summer of Code program. A great thank you to
Dongcan and to Google for making this possible.w and
W) default to NULL, which corresponds to uniform
weights.weightedVar(x, w) used the wrong bias correction
factor resulting in an estimate that was tau too large, where
tau = ((sum(w) - 1) / sum(w)) / ((length(w) - 1) / length(w)).
Thanks to Wolfgang Abele for reporting and troubleshooting on
this.
weightedVar(x) with length(x) = 1
returned 0 - not NA. Same for weightedSd().
weightedMedian(x, w = NA_real_) returned
x rather than NA_real_. This only happened for
length(w) = 1.
allocArray(dim) failed for
prod(dim) >= .Machine$integer.max.
CLEANUP: Defunct argument centers for
col-/rowMads(); use
center.
weightedVar(x, w, method = "0.14.2") is
deprecated.
x_OP_y() and t_tx_OP_y() would return
garbage on Solaris SPARC (and possibly other architectures as well) when
input was integer and had missing values.product(x, na.rm = FALSE) for integer x
with both zeros and NAs returned zero rather than NA.
weightedMean(x, w, na.rm = TRUE) did not handle
missing values in x properly, if it was an integer. It
would also return NaN if there were weights w with missing
values, whereas stats::weighted.mean() would skip such data
points. Now weightedMean() does the same.
(col|row)WeightedMedians() did not handle infinite
weights as weightedMedian() does.
x_OP_y(x, y, OP, na.rm = FALSE) returned garbage iff
x or y had missing values of type
integer.
rowQuantiles() and rowIQRs() did not
work for single-row matrices. Analogously for the corresponding column
functions.
rowCumsums(), rowCumprods()
rowCummins(), and rowCummaxs(), accessed
out-of-bound elements for Nx0 matrices where N > 0. The corresponding
column methods has similar memory errors for 0xK matrices where K >
0.
anyMissing(list(NULL)) returned NULL; now
FALSE.
rowCounts() resulted in garbage if a previous column
had NAs (because it forgot to update index kk in such cases).
rowCumprods(x) handled missing values and zeros
incorrectly for integer x (not double); a zero would trump
an existing missing value causing the following cumulative products to
become zero. It was only a zero that trumped NAs; any other integer
would work as expected. Note, this bug was not in
colCumprods().
rowAnys(x, value, na.rm = FALSE) did not handle
missing values in a numeric x properly. Similarly, for
non-numeric and non-logical x, row- and
colAnys(), row- and colAlls(),
anyValue() and allValue() did not handle when
value was a missing value.
All of the above bugs were identified and fixed by Dongcan Jiang (Peking University, China), who also added corresponding unit tests.
anyMissing() is no longer an S4 generic. This
was done as part of the migration of making all functions of
matrixStats plain R functions, which minimizes calling
overhead and it will also allow us to drop methods from
the package dependencies. I’ve scanned all CRAN and Bioconductor
packages depending on matrixStats and none of them
relied on anyMissing() dispatching on class, so hopefully
this move has little impact. The only remaining S4 methods are now
colMedians() and rowMedians().CONSISTENCY: Renamed argument centers of
col-/rowMads() to center. This is
consistent with col-/rowVars().
CONSISTENCY: col-/rowVars() now use
na.rm = FALSE as the default (na.rm = TRUE was
mistakenly introduced as the default in v0.9.7).
SPEEDUP: The check for user interrupts at the C level is now done
less frequently of the functions. It does every k:th iteration, where
k = 2^20, which is tested for using
(iter % k == 0). It turns out, at least with the default
compiler optimization settings that I use, that this test is 3 times
faster if k = 2^n where n is an integer. The following
functions checks for user interrupts: logSumExp(),
(col|row)LogSumExps(), (col|row)Medians(),
(col|row)Mads(), (col|row)Vars(), and
(col|row)Cum(Min|Max|prod|sum)s().
SPEEDUP: logSumExp(x) is now faster if
x does not contain any missing values. It is also faster if
all values are missing or the maximum value is +Inf - in both cases it
can skip the actual summation step.
all() and any() flavored methods on
non-numeric and non-logical (e.g. character) vectors and matrices with
na.rm = FALSE did not give results consistent with
all() and any() if there were missing values.
For example, with x <- c("a", NA, "b") we have
all(x == "a") == FALSE and
any(x == "a") == TRUE, whereas our corresponding methods
would return NA in those cases. The methods fixed are
allValue(), anyValue(),
col-/rowAlls(), and
col-/rowAnys(). Added more package tests to
cover these cases.
logSumExp(x, na.rm = TRUE) would return NA if all
values were NA and length(x) > 1. Now it returns -Inf
for all length(x):s.
diff2() with differences >= 3 would
read spurious values beyond the allocated memory. This error,
introduced in 0.13.0, was harmless in the sense that the returned value
was unaffected and still correct. Thanks to Brian Ripley and the CRAN
check tools for catching this. I could reproduce it locally with
valgrind.anyMissing() and rowMedians().Added weightedMean(), which is ~10 times faster than
stats::weighted.mean().
Added count(x, value) which is a notably faster than
sum(x == value). This can also be used to count missing
values etc.
Added allValue() and anyValue() for
all(x == value) and any(x == value).
Added diff2(), which is notably faster than
base::diff() for vectors, which it is designed
for.
Added iqrDiff() and
(col|row)IqrDiffs().
CONSISTENCY: Now rowQuantiles(x, na.rm = TRUE)
returns all NAs for rows with missing values. Analogously for
colQuantiles(), colIQRs(),
rowIQRs() and iqr(). Previously, all these
functions gave an error saying missing values are not allowed.
COMPLETENESS: Added corresponding “missing” vector functions for
already existing column and row functions. Similarly, added “missing”
column and row functions for already existing vector functions,
e.g. added iqr() and count() to complement
already existing (col|row)IQRs() and
(col|row)Counts() functions.
ROBUSTNESS: Now column and row methods give slightly more informative error messages if a data.frame is passed instead of a matrix.
SPEEDUP: (col|row)Diffs() are now implemented in
native code and notably faster than diff() for
matrices.
SPEEDUP: Made binCounts() and
binMeans() a bit faster.
SPEEDUP: Implemented weightedMedian() in native
code, which made it ~3-10 times faster. Dropped support for
ties = "both", because it would have to return two values
in case of ties, which made the API unnecessarily complicated. If really
needed, then call the function twice with ties = "min" and
ties = "max".
SPEEDUP: (col|row)Anys() and
(col|row)Alls() is now notably faster compared to previous
versions.
anyMissing() into a
plain R function, the specific anyMissing() implementations
for data.frame:s and and list:s were dropped and is now handled by
anyMissing() for "ANY", which is the only S4
method remaining now. In a near future release, this remaining
"ANY" method will turned into a plain R function and the
current S4 generic will be dropped. We know of no CRAN and Bioconductor
packages that rely on it being a generic function. Note also that since
R (>= 3.1.0) there is a base::anyNA() function that does
the exact same thing making anyMissing() obsolete.weightedMedian(..., ties = "both") would give an error
if there was a tie. Added package test for this case.weightedMedian(..., ties = "both") is now defunct.product() on integer
vector incorrectly used C-level abs() on intermediate
values despite those being doubles requiring fabs().
Despite this, the calculated product would still be correct (at least
when validated on several local setups as well as on the CRAN servers).
Again, thanks to Brian Ripley for pointing out another invalid
integer-double coercion at the C level.weightedMedian(..., interpolate = FALSE, ties = "both")
is defunct.(col|row)Cumsums(x) where x is integer
would return garbage for columns (rows) containing missing
values.
rowMads(x) where x is numeric (not
integer) would give incorrect results for rows that had an odd
number of values (no ties). Analogously issues with
colMads(). Added package tests for such cases too. Thanks
to Brian Ripley and the CRAN check tools for (yet again) catching
another coding mistake. Details: This was because the C-level
calculation of the absolute value of residuals toward the median would
use integer-based abs() rather than double-based
fabs(). Now it fabs() is used when the values
are double and abs() when they are integers.
(col|row)Cumsums(),
(col|row)Cumprods(), (col|row)Cummins(), and
(col|row)Cummaxs().(col|row)WeightedMeans() with all zero weights gave
mean estimates with values 0 instead of NaN.SPEEDUP: Implemented (col|row)Mads(),
(col|row)Sds(), and (col|row)Vars() in native
code.
SPEEDUP: Made (col|row)Quantiles(x) faster for
x without missing values (and default
type = 7L quantiles). It should still be implemented in
native code though.
SPEEDUP: Made rowWeightedMeans() faster.
(col|row)Medians(x) when x is integer
would give invalid median values in case (a) it was calculated as the
mean of two values (“ties”), and (b) the sum of those values where
greater than .Machine$integer.max. Now such ties are
calculated using floating point precision. Add lots of package
tests.SPEEDUP: Now (col|row)Mins(),
(col|row)Maxs(), and (col|row)Ranges() are
implemented in native code providing a significant speedup.
SPEEDUP: Now colOrderStats() also is implemented in
native code, which indirectly makes colMins(),
colMaxs() and colRanges() faster.
SPEEDUP: colTabulates(x) no longer uses
rowTabulates(t(x)).
SPEEDUP: colQuantiles(x) no longer uses
rowQuantiles(t(x)).
flavor of
(col|row)Ranks() is now ignored.(col|row)Prods() now uses default
method = "direct" (was "expSumLog").SPEEDUP: Now colCollapse(x) no longer utilizes
rowCollapse(t(x)). Added package tests for
(col|row)Collapse().
SPEEDUP: Now colDiffs(x) no longer uses
rowDiffs(t(x)). Added package tests for
(col|row)Diffs().
SPEEDUP: Package no longer utilizes match.arg() due
to its overhead; methods sumOver(),
(col|row)Prods() and (col|row)Ranks() were
updated.
dim. For instance,
rowCounts(x, dim = c(nrow, ncol)) is the same as
rowCounts(matrix(x, nrow, ncol)), but more efficient since
it avoids creating/allocating a temporary matrix.colCounts() is implemented in native code.
Moreover, (col|row)Counts() are now also implemented in
native code for logical input (previously only for integer and double
input). Added more package tests and benchmarks for these
functions.sdDiff(), madDiff(),
varDiff(), weightedSd(),
weightedVar() and weightedMad() into plain
functions (were generic functions).::.indexByRow() in native code and it
is no longer a generic function, but a regular function, which is also
faster to call. The first argument of indexByRow() has been
changed to dim such that one should use
indexByRow(dim(X)) instead of indexByRow(X) as
in the past. The latter form is still supported, but deprecated.allocVector(), allocMatrix(), and
allocArray() for faster allocation numeric vectors,
matrices and arrays, particularly when filled with non-missing
values.indexByRow(X) with a matrix X is
deprecated. Instead call it with indexByRow(dim(X)).Better support for long vectors.
PRECISION: Using greater floating-point precision in more internal intermediate calculations, where possible.
binCounts() and binMeans() it is possible that
a bin gets a higher count than what can be represented by an R integer
(.Machine$integer.max = 2^31-1). If that happens, an
informative warning is generated and the bin count is set to
.Machine$integer.max. If this happens for
binMeans(), the corresponding mean is still properly
calculated and valid..Call() and takes care of most of the argument validation
and construction of the return value. This function dispatch to
functions in the low-level API based on data type(s) and other
arguments. The low-level API is written to work with basic C data types
only.R_xlen_t on R (>=
3.0.0) systems where LONG_VECTOR_SUPPORT is not
supported.sumOver() and meanOver(), which are
notably faster versions of sum(x[idxs]) and
mean(x[idxs]). Moreover, instead of having to do
sum(as.numeric(x)) to avoid integer overflow when
x is an integer vector, one can do
sumOver(x, mode = "numeric"), which avoids the extra
copy created when coercing to numeric (this numeric copy is also twice
as large as the integer vector). Added package tests and benchmark
reports for these functions.SPEEDUP: Made anyMissing(),
logSumExp(), (col|row)Medians(), and
(col|row)Counts() slightly faster by making the native code
assign the results directly to the native vector instead of to the R
vector, e.g. ansp[i] = v where
ansp = REAL(ans) instead of
REAL(ans)[i] = v.
Added benchmark reports for anyMissing() and
logSumExp().
binMeans() returned 0.0 instead of
NA_real_ for empty bins."redefinition of typedef 'R_xlen_t'".Added benchmark reports for also non-matrixStats
functions col-/rowSums() and
col-/rowMeans().
Now all colNnn() and rowNnn() methods
are benchmarked in a combined report making it possible to also compare
colNnn(x) with rowNnn(t(x)).
Relaxed some packages tests such that they assert numerical
correctness via all.equal() rather than
identical().
Submitted to CRAN.
product() incorrectly assumed
that the value of prod(c(NaN, NA)) is uniquely defined.
However, as documented in help("is.nan"), it may be NA or
NaN depending on R system/platform.Introduced a bug in v0.9.5 causing
col-/rowVars() and hence also
col-/rowSds() to return garbage. Add package
tests for these now.
Submitted to CRAN.
signTabulate() for tabulating the number of
negatives, zeros, positives and missing values. For doubles, the number
of negative and positive infinite values are also counted.SPEEDUP: Now col-/rowProds() utilizes
new product() function.
SPEEDUP: Added product() for calculating the product
of a numeric vector via the logarithm.
SPEEDUP: Made weightedMedian() a plain function (was
an S3 method).
CLEANUP: Now only exporting plain functions and generic functions.
SPEEDUP: Turned more S4 methods into S3 methods,
e.g. rowCounts(), rowAlls(),
rowAnys(), rowTabulates() and
rowCollapse().
method to
col-/rowProds() for controlling how the
product is calculated.SPEEDUP: Package is now byte compiled.
SPEEDUP: Made rowProds() and
rowTabulates() notably faster.
SPEEDUP: Now rowCounts(), rowAnys(),
rowAlls() and corresponding column methods can search for
any value in addition to the default TRUE. The search for a matching
integer or double value is done in native code, which is notably faster
(and more memory efficient because it avoids creating any new
objects).
SPEEDUP: Made colVars() and colSds()
notably faster and rowVars() and rowSds() a
slightly bit faster.
Added benchmark reports,
e.g. matrixStats:::benchmark("colMins").
indexByRow(), madDiff(),
sdDiff() and varDiff().trim to madDiff(),
sdDiff() and varDiff().binMeans(x, bx) would try to access
an out-of-bounds value of argument y iff x
contained elements that are left of all bins in bx. This
bug had no impact on the results and since no assignment was done it
should also not crash/core dump R. This was discovered thanks to new
memtests (ASAN and valgrind) provided by CRAN.rowProds() would throw
"Error in rowSums(isNeg) :xmust be an array of at least two dimensions"
on matrices where all rows contained at least one zero. Thanks to Roel
Verbelen at KU Leuven for the report.weighedVar() and weightedSd().MEMORY: Updated all functions to do a better job of cleaning out temporarily allocated objects as soon as possible such that the garbage collector can remove them sooner, iff wanted. This increase the chance for a smaller memory footprint.
Submitted to CRAN.
right to binCounts() and
binMeans() to specify whether binning should be done by
(u,v] or [u,v). Added system tests validating the correctness of the two
cases.anyMissing() everywhere
possible.ROBUSTNESS: Now importing loadMethod from
methods package such that matrixStats
S4-based methods also work when methods is not loaded,
e.g. when Rscript is used, cf. Section ‘Default packages’
in ‘R Installation and Administration’.
ROBUSTNESS: Updates package system tests such that the can run with only the base package loaded.
CLEANUP: Now only importing two functions from the methods package.
Bumped up package dependencies.
quietly of
library()/require().help("rowQuantiles").(col|row)Mins() and
(col|row)Maxs() much faster.rowRanges(x) on an Nx0 matrix would give an error. Same
for colRanges(x) on an 0xN matrix. Added system tests for
these and other special cases.(col|row)WeightedMedians().(col|row)Tabulates() by replacing
rm() calls with NULL assignments.\usage{} lines are at most 90
characters long.binCounts() and binMeans() now
uses Hoare’s Quicksort for presorting x before
counting/averaging. They also no longer test in every iteration (== for
every data point) whether the last bin has been reached or not, but only
after completing a bin.logSumExp() used an invalid check for
missing value of an integer argument. Detected by Brian Ripley upon CRAN
submission.logSumExp(lx) and
(col|row)LogSumExps(lx) for accurately computing of
log(sum(exp(lx))) for standalone vectors, and row and
column vectors of matrices. Thanks to Nakayama (Japan) for the
suggestion and contributing a draft in R.preserveShape to
colRanks(). For backward compatibility the default is
preserveShape = FALSE, but it may change in the
future.Since v0.6.4, (col|row)Ranks() gave the incorrect
results for integer matrices with missing values.
Since v0.6.4, (col|row)Medians() for integers would
calculate ties as floor(tieAvg).
(col|row)Ranks() support "max"
(default), "min" and "average" for argument
ties.method. Added system tests validation these cases.
Thanks Peter Langfelder (UCLA) for contributing this.ties.method to rowRanks()
and colRanks(), but still only support for
"max" (as before).anyMissing() for data type raw,
which always returns FALSE.ROBUSTNESS: Added system test for
anyMissing().
ROBUSTNESS: Now S3 methods are declared in the namespace.
example(weightedMedian) faster.In some cases binCounts() and
binMeans() could try to go past the last bin resulting a
core dump.
binCounts() and binMeans() would return
random/garbage values for bins that were beyond the last data
point.
Added binMeans() for fast sample-mean calculation in
bins. Thanks to Martin Morgan at the Fred Hutchinson Cancer Research
Center, Seattle, for contributing the core code for this.
Added binCounts() for fast element counting in
bins.
.Internal(psort(...)) call
with a call to a new internal partial sorting function, which utilizes
the native rPsort() part of the R internals.(col|row)Prods() handle missing
values.(col|row)Prods() would return NA
instead of 0 for some elements. Added a redundancy test for the case.
Thanks Brenton Kenkel at University of Rochester for reporting on
this.Added weightedMad() from aroma.core
v2.5.0.
Added weightedMedian() from
aroma.light v1.25.2.
This package no longer depends on the aroma.light package for any of its functions.
Now this package only imports R.methodsS3, meaning it no longer loads R.methodsS3 when it is loaded.
centers of
rowMads()/colMads() to explicitly be
(col|row)Medians(x,...). The default behavior has not
changed.ROBUSTNESS: Added system/redundancy tests for
rowMads()/colMads().
CRAN: Made the system tests “lighter” by default, but full tests
can still be run, cf. tests/*.R scripts.
colMads() would return the incorrect estimates. This
bug was introduced in matrixStats v0.4.0
(2011-11-11).rowMedians(..., na.rm = TRUE) did not handle NaN (only
NA). The reason for this was the the native code used
ISNA() to test for NA and NaN, but it should have been
ISNAN(), which is opposite to how is.na() and
is.nan() at the R level work. Added system tests for this
case.rowAvgsPerColSet() and
colAvgsPerRowSet().Added help pages with an example to rowIQRs() and
colIQRs().
Added example to rowQuantiles().
rowIQRs() and colIQRs() would return the
25% and the 75% quantiles, not the difference between them. Thanks
Pierre Neuvial at CNRS, Evry, France for the report.center
in rowMads() and colMads(). It added
unnecessary overhead if not needed.rowRanks() and colRanks(). Thanks
Hector Corrada Bravo (University of Maryland) and Harris Jaffee (John
Hopkins).colMedians(x) no longer uses
rowMedians(t(x)); instead there is now an optimized
native-code implementation. Also, colMads() utilizes the
new colMedians() directly. This improvement was kindly
contributed by Harris Jaffee at Biostatistics of John Hopkins, USA.colMedians() and
rowMedians().(col|row)Quantiles() contains column
names..First.lib() and
.Last.lib().(col|row)WeightedMeans(..., na.rm = TRUE) would
incorrectly treat missing values as zeros. Added corresponding
redundancy tests (also for the median case). Thanks Pierre Neuvial for
reporting this.colRanges(x) would return a matrix of wrong
dimension if x did not have any missing values. This would
affect all functions relying on colRanges(),
e.g. colMins() and colMaxs(). Added a
redundancy test for this case. Thanks Pierre Neuvial at UC Berkeley for
reporting this.
(col|row)Ranges() return a matrix with dimension
names.
"%#x" in
rowTabulates() when creating the column names of the result
matrix. It gave an error OSX with R v2.9.0 devel (2009-01-13 r47593b)
current the OSX server at R-forge.rowWeightedMedians() to
run conditionally on aroma.light, which is only a
suggested package - not a required one. This in order to prevent
R CMD check to fail on CRAN, which prevents it for
building binaries (as it currently happens on their OSX servers).rowOrderStats(), the stack would not
become UNPROTECTED before calling error.(col|row)Weighted(Mean|Median)s() for
weighted averaging.R CMD check flawlessly.(col|row)Tabulates() for integer and raw
matrices.rowCollapse() was broken and returned the wrong
elements.Added (col|row)Collapse().
Added varDiff(), sdDiff(), and
madDiff().
Added indexByRow().
Added (col|row)OrderStats().
Added (col|row)Ranges() and
(col|row)(Min|Max)s().
Added colMedians().
Now anyMissing() support most data types as
structures.
Imported the rowNnn() methods from
Biobase.
Created.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.