doesn't depend on anything, so it wouldn't get automatically updated so an
old package built with a pkg_create which used @ignore annotations wouldn't
have been replaced. pkg_add warning reported by kettenis@.
* the \r character was not handled correctly
* Added support for flexible tabize wished
* some highlighting mistakes were introduced by the last
bugfix.
Tested on i386. While here USE_GROFF is not needed.
OK okan@, aja@
GtkSpell provides word-processor-style highlighting and replacement of
misspelled words in a GtkTextView widget. Right-clicking a misspelled
word pops up a menu of suggested replacements.
ok jasper@
GCC hates and uses 800MB+ to compile, and embedding with .incbin.
Switch the port to using .incbin. Fixes out of memory on alpha reported
and tested by naddy@, greatly improves build time on arm.
The VMEM_WARNING can now be removed.
- while there, don't use groff.
Latexmk is a perl script for running LaTeX the correct number of times
to resolve cross references, etc; it also runs auxiliary programs
(bibtex, makeindex if necessary, and dvips and/or a previewer as
requested).
<...>
ok jasper@
This module provides a utility method, "to_identifier" for converting
an arbitrary string into a readable representation using the ASCII
subset of "\w" for use as an identifier in a computer program. The
intent is to make unique identifier names from which the content
of the original string can be easily inferred by a human just by
reading the identifier.
If you need the full set of "\w" including Unicode, see the subclass
String::ToIdentifier::EN::Unicode.
Currently, this process is one way only, and will likely remain
this way.
The default is to create camelCase identifiers, or you may pass in
a separator char of your choice such as "_".
Binary char groups will be separated by "_" even in camelCase
identifiers to make them easier to read, e.g.: "foo_2_0xFF_Bar".
The module is a probability based, corpus-trained tagger that assigns
POS tags to English text based on a lookup dictionary and a set of
probability values. The tagger assigns appropriate tags based on
conditional probabilities - it examines the preceding tag to determine
the appropriate tag for the current word. Unknown words are classified
according to word morphology or can be set to be treated as nouns
or other parts of speech. The tagger also extracts as many nouns
and noun phrases as it can, using a set of regular expressions.
The exportable subroutines of Lingua::EN::Inflect provide
plural inflections, "a"/"an" selection for English words,
and manipulation of numbers as words.
