Democratic candidates are preferred by a point margin in Democratic-held districts, while Republican candidates are preferred by a point margin in Republican-held districts. Abortion is another prominent issue in this election. When asked about the importance of abortion rights, 61 percent of likely voters say the issue is very important in determining their vote for Congress and another 20 percent say it is somewhat important; just 17 percent say it is not too or not at all important.

With the controlling party in Congress hanging in the balance, 51 percent of likely voters say they are extremely or very enthusiastic about voting for Congress this year; another 29 percent are somewhat enthusiastic while 19 percent are either not too or not at all enthusiastic.

Today, Democrats and Republicans have about equal levels of enthusiasm, while independents are much less likely to be extremely or very enthusiastic. As Californians prepare to vote in the upcoming midterm election, fewer than half of adults and likely voters are satisfied with the way democracy is working in the United States—and few are very satisfied. Satisfaction was higher in our February survey when 53 percent of adults and 48 percent of likely voters were satisfied with democracy in America.

Today, half of Democrats and about four in ten independents are satisfied, compared to about one in five Republicans. Notably, four in ten Republicans are not at all satisfied. In addition to the lack of satisfaction with the way democracy is working, Californians are divided about whether Americans of different political positions can still come together and work out their differences.

Forty-nine percent are optimistic, while 46 percent are pessimistic. Today, in a rare moment of bipartisan agreement, about four in ten Democrats, Republicans, and independents are optimistic that Americans of different political views will be able to come together.

Notably, in , half or more across parties, regions, and demographic groups were optimistic. Today, about eight in ten Democrats—compared to about half of independents and about one in ten Republicans—approve of Governor Newsom. Across demographic groups, about half or more approve of how Governor Newsom is handling his job.

Approval of Congress among adults has been below 40 percent for all of after seeing a brief run above 40 percent for all of Democrats are far more likely than Republicans to approve of Congress.

Fewer than half across regions and demographic groups approve of Congress. Approval in March was at 44 percent for adults and 39 percent for likely voters. Across demographic groups, about half or more approve among women, younger adults, African Americans, Asian Americans, and Latinos.

Views are similar across education and income groups, with just fewer than half approving. Approval in March was at 41 percent for adults and 36 percent for likely voters. Across regions, approval reaches a majority only in the San Francisco Bay Area. Across demographic groups, approval reaches a majority only among African Americans. This map highlights the five geographic regions for which we present results; these regions account for approximately 90 percent of the state population.

Residents of other geographic areas in gray are included in the results reported for all adults, registered voters, and likely voters, but sample sizes for these less-populous areas are not large enough to report separately.

The PPIC Statewide Survey is directed by Mark Baldassare, president and CEO and survey director at the Public Policy Institute of California. Coauthors of this report include survey analyst Deja Thomas, who was the project manager for this survey; associate survey director and research fellow Dean Bonner; and survey analyst Rachel Lawler.

The Californians and Their Government survey is supported with funding from the Arjay and Frances F. Findings in this report are based on a survey of 1, California adult residents, including 1, interviewed on cell phones and interviewed on landline telephones.

The sample included respondents reached by calling back respondents who had previously completed an interview in PPIC Statewide Surveys in the last six months. Interviews took an average of 19 minutes to complete.

Interviewing took place on weekend days and weekday nights from October 14—23, Cell phone interviews were conducted using a computer-generated random sample of cell phone numbers. Additionally, we utilized a registration-based sample RBS of cell phone numbers for adults who are registered to vote in California.

All cell phone numbers with California area codes were eligible for selection. After a cell phone user was reached, the interviewer verified that this person was age 18 or older, a resident of California, and in a safe place to continue the survey e. Cell phone respondents were offered a small reimbursement to help defray the cost of the call.

Cell phone interviews were conducted with adults who have cell phone service only and with those who have both cell phone and landline service in the household. Landline interviews were conducted using a computer-generated random sample of telephone numbers that ensured that both listed and unlisted numbers were called.

Additionally, we utilized a registration-based sample RBS of landline phone numbers for adults who are registered to vote in California. All landline telephone exchanges in California were eligible for selection.

For both cell phones and landlines, telephone numbers were called as many as eight times. When no contact with an individual was made, calls to a number were limited to six. Also, to increase our ability to interview Asian American adults, we made up to three additional calls to phone numbers estimated by Survey Sampling International as likely to be associated with Asian American individuals.

Accent on Languages, Inc. The survey sample was closely comparable to the ACS figures. To estimate landline and cell phone service in California, Abt Associates used state-level estimates released by the National Center for Health Statistics—which used data from the National Health Interview Survey NHIS and the ACS.

The estimates for California were then compared against landline and cell phone service reported in this survey. We also used voter registration data from the California Secretary of State to compare the party registration of registered voters in our sample to party registration statewide.

The sampling error, taking design effects from weighting into consideration, is ±3. This means that 95 times out of , the results will be within 3. The sampling error for unweighted subgroups is larger: for the 1, registered voters, the sampling error is ±4. For the sampling errors of additional subgroups, please see the table at the end of this section. Sampling error is only one type of error to which surveys are subject. Results may also be affected by factors such as question wording, question order, and survey timing.

We present results for five geographic regions, accounting for approximately 90 percent of the state population. Residents of other geographic areas are included in the results reported for all adults, registered voters, and likely voters, but sample sizes for these less-populous areas are not large enough to report separately.

We also present results for congressional districts currently held by Democrats or Republicans, based on residential zip code and party of the local US House member. We compare the opinions of those who report they are registered Democrats, registered Republicans, and no party preference or decline-to-state or independent voters; the results for those who say they are registered to vote in other parties are not large enough for separate analysis.

We also analyze the responses of likely voters—so designated per their responses to survey questions about voter registration, previous election participation, intentions to vote this year, attention to election news, and current interest in politics. The percentages presented in the report tables and in the questionnaire may not add to due to rounding.

Additional details about our methodology can be found at www. pdf and are available upon request through surveys ppic. October 14—23, 1, California adult residents; 1, California likely voters English, Spanish.

Margin of error ±3. Percentages may not add up to due to rounding. Overall, do you approve or disapprove of the way that Gavin Newsom is handling his job as governor of California? Overall, do you approve or disapprove of the way that the California Legislature is handling its job? Do you think things in California are generally going in the right direction or the wrong direction?

Thinking about your own personal finances—would you say that you and your family are financially better off, worse off, or just about the same as a year ago? Next, some people are registered to vote and others are not. Are you absolutely certain that you are registered to vote in California? Are you registered as a Democrat, a Republican, another party, or are you registered as a decline-to-state or independent voter?

Would you call yourself a strong Republican or not a very strong Republican? Do you think of yourself as closer to the Republican Party or Democratic Party? Which one of the seven state propositions on the November 8 ballot are you most interested in? Hopefully in a few future releases, the limitations become less. JSONLab is an open-source project. This means you can not only use it and modify it as you wish, but also you can contribute your changes back to JSONLab so that everyone else can enjoy the improvement.

For anyone who want to contribute, please download JSONLab source code from its source code repositories by using the following command:. Sometimes, you may find it is necessary to modify JSONLab to achieve your goals, or attempt to modify JSONLab functions to fix a bug that you have encountered.

If you are happy with your changes and willing to share those changes to the upstream author, you are recommended to create a pull-request on github. To create a pull-request, you first need to "fork" jsonlab on Github by clicking on the "fork" button on top-right of JSONLab's github page. Once you forked jsonlab to your own directory, you should then implement the changes in your own fork.

Then type in the description of the changes. You are responsible to format the code updates using the same convention tab-width: 8, indentation: 4 spaces as the upstream code. We appreciate any suggestions and feedbacks from you. Please use the following mailing list to report any questions you may have regarding JSONLab:. The loadjson. m function was significantly modified from the earlier parsers BSD 3-clause licensed written by the below authors.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:. Skip to content. Star BSDClause license. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Branches Tags. Could not load branches. Could not load tags. A tag already exists with the provided branch name.

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Failed to load latest commit information. View code. m loadjson. m savebj. m saveubjson. m as an alias loadbj. m loadubjson. m as an alias jdataencode. m jdatadecode. m jload. m Sharing JSONLab created data files in Python Known Issues and TODOs Contribution and feedback Acknowledgement loadjson.

m loadmsgpack. m zlibdecode. m, zlibencode. m, gzipencode. The contents of the inst subdirectory will be copied recursively to the installation directory. Subdirectories of inst should not interfere with those used by R currently, R , data , demo , exec , libs , man , help , html and Meta , and earlier versions used latex , R-ex.

The copying of the inst happens after src is built so its Makefile can create files to be installed. To exclude files from being installed, one can specify a list of exclude patterns in file. Rinstignore in the top-level source directory. These patterns should be Perl-like regular expressions see the help for regexp in R for the precise details , one per line, to be matched case-insensitively against the file and directory paths, e. packages on the tarball. So any information files you wish an end user to see should be included in inst.

Note that if the named exceptions also occur in inst , the version in inst will be that seen in the installed package. Things you might like to add to inst are a CITATION file for use by the citation function, and a NEWS. Rd file for use by the news function. See its help page for the specific format restrictions of the NEWS. Rd file. Subdirectory tests is for additional package-specific test code, similar to the specific tests that come with the R distribution.

Test code can either be provided directly in a. Rin file containing code which in turn creates the corresponding. R file e. The results of running a. R file are written to a. Rout file. If there is a corresponding save file, these two are compared, with differences being reported but not causing an error. Note that the package-specific tests are run in a vanilla R session without setting the random-number seed, so tests which use random numbers will need to set the seed to obtain reproducible results and it can be helpful to do so in all cases, to avoid occasional failures when tests are run.

If directory tests has a subdirectory Examples containing a file pkg -Ex. save , this is compared to the output file for running the examples when the latter are checked. Reference output should be produced without having the --timings option set and note that --as-cran sets it. Things which trip up maintainers include displayed version numbers from loading other packages, printing numerical results to an unreproducibly high precision and printing timings. Another trap is small values which are in fact rounding error from zero: consider using zapsmall.

Subdirectory exec could contain additional executable scripts the package needs, typically scripts for interpreters such as the shell, Perl, or Tcl. Note too that this is not suitable for executable programs since some platforms including Windows support multiple architectures using the same installed package directory. Subdirectory po is used for files related to localization : see Internationalization. Subdirectory tools is the preferred place for auxiliary files needed during configuration, and also for sources need to re-create scripts e.

M4 files for autoconf : some prefer to put those in a subdirectory m4 of tools. Next: Non-R scripts in packages , Previous: Package subdirectories , Up: Package structure [ Contents ][ Index ]. The data subdirectory is for data files, either to be made available via lazy-loading or for loading using data. Data files can have one of three types as indicated by their extension: plain R code. r , tables. tab ,. txt , or. csv , see? data for the file formats, and note that.

csv is not the standard 22 CSV format , or save images. RData or. The files should not be hidden have names starting with a dot. Images extensions. RData 23 or. rda can contain references to the namespaces of packages that were used to create them. Preferably there should be no such references in data files, and in any case they should only be to packages listed in the Depends and Imports fields, as otherwise it may be impossible to install the package. To check for such references, load all the images into a vanilla R session, run str on all the datasets, and look at the output of loadedNamespaces.

Particular care is needed where a dataset or one of its components is of an S4 class, especially if the class is defined in a different package.

First, the package containing the class definition has to be available to do useful things with the dataset, so that package must be listed in Imports or Depends even if this gives a check warning about unused imports. Second, the definition of an S4 class can change, and often is unnoticed when in a package with a different author. So it may be wiser to use the. R scripts to produce your data, loading your namespace, you can speed up installation by providing a file datalist in the data subdirectory.

csv files can be compressed by gzip , bzip2 or xz , optionally with additional extension. bz2 or. If your package is to be distributed, do consider the resource implications of large datasets for your users: they can make packages very slow to download and use up unwelcome amounts of storage space, as well as taking many seconds to load.

It is normally best to distribute large datasets as. Using bzip2 or xz compression will usually reduce the size of both the package tarball and the installed package, in some cases by a factor of two or more. Package tools has a couple of functions to help with data images: checkRdaFiles reports on the way the image was saved, and resaveRdaFiles will re-save with a different type of compression, including choosing the best type for that particular image.

Useful values are bzip2 , xz and the default, gzip : value none is also accepted. rdb file. A function to do that quoting sizes in KB is. If you see that, run CheckLazyDataCompression and set the field — to gzip in the unlikely event 24 that is the best choice. The analogue for sysdata.

Lazy-loading is not supported for very large datasets those which when serialized exceed 2GB, the limit for the format on bit platforms.

Next: Specifying URLs , Previous: Data in packages , Up: Package structure [ Contents ][ Index ]. Subdirectory exec could be used for scripts for interpreters such as the shell, BUGS, JavaScript, Matlab, Perl, php amap , Python or Tcl Simile , or even R. Java code is a special case: except for very small programs,. java files should be byte-compiled to a.

class file and distributed as part of a. jar file: the conventional location for the. It is desirable and required under an Open Source license to make the Java source files available: this is best done in a top-level java directory in the package—the source files should not be installed.

This is fairly easy to do: first find the Tcl search path:. If no location on that search path is writeable, you will need to add one each time BWidget is to be used with tcltk::addTclPath. gz , but needed patching for current Tk 8.

Previous: Non-R scripts in packages , Up: Package structure [ Contents ][ Index ]. URLs in many places in the package documentation will be converted to clickable hyperlinks in at least some of their renderings.

So care is needed that their forms are correct and portable. Spaces in URLs are not portable and how they are handled does vary by HTTP server and by client. Next: Checking and building packages , Previous: Package structure , Up: Creating R packages [ Contents ][ Index ]. Note that most of this section is specific to Unix-alikes: see the comments later on about the Windows port of R.

If your package needs some system-dependent configuration before installation you can include an executable Bourne 26 shell script configure in your package which if present is executed by R CMD INSTALL before any other action is performed.

This can be a script created by the Autoconf mechanism, but may also be a script written by yourself. Use this to detect if any nonstandard libraries are present such that corresponding code in the package can be disabled at install time rather than giving error messages when the package is compiled or used.

To summarize, the full power of Autoconf is available for your extension package including variable substitution, searching for libraries, etc. Under a Unix-alike only, an executable Bourne shell script cleanup is executed as the last thing by R CMD INSTALL if option --clean was given, and by R CMD build when preparing the package for building from its source.

As an example consider we want to use functionality provided by a C or Fortran library foo. For example, if a function named bar is to be made available by linking against library foo i. From this file configure creates the actual R source file foo. R looking like. if library foo was not found with the desired functionality.

In this case, the above R code effectively disables the function. One could also use different file fragments for available and missing functionality, respectively. You will very likely need to ensure that the same C compiler and compiler flags are used in the configure tests as when compiling R or your package.

Under a Unix-alike, you can achieve this by including the following fragment early in configure. If your code does load checks for example, to check for an entry point in a library or to run code then you will also need. You can use R CMD config to get the value of the basic configuration variables, and also the header and library flags necessary for linking a front-end executable program against R, see R CMD config --help for details. Note that FLIBS as determined by R must be used to ensure that Fortran code works on all R platforms.

Otherwise R CMD build may ship the files that are created. For example, package RODBC has. As this example shows, configure often creates working files such as config. If your configure script needs auxiliary files, it is recommended that you ship them in a tools directory as R itself does. You should bear in mind that the configure script will not be used on Windows systems.

If your package is to be made publicly available, please give enough information for a user on a non-Unix-alike platform to configure it manually, or provide a configure. win script or configure. ucrt to be used on that platform.

Optionally, there can be a cleanup. win script or cleanup. Both should be shell scripts to be executed by ash , which is a minimal version of Bourne-style sh. When configure. win or configure. In some rare circumstances, the configuration and cleanup scripts need to know the location into which the package is being installed. Usually, the object that is dynamically loaded by R is linked against the second, dependent, object. On some systems, we can add the location of this dependent object to the object that is dynamically loaded by R.

Another example is when a package installs support files that are required at run time, and their location is substituted into an R data structure at installation time.

The names of the top-level library directory i. Additionally, the name of the package e. Its main use is in configure. One of the more tricky tasks can be to find the headers and libraries of external software. One tool which is increasingly available on Unix-alikes but not by default 28 on macOS to do this is pkg-config.

Note that pkg-config --libs gives the information required to link against the default version 30 of that library usually the dynamic one , and pkg-config --static --libs may be needed if the static library is to be used. Static libraries are commonly used on macOS and Windows to facilitate bundling external software with binary distributions of packages. This means that portable source packages need to allow for this.

so which would be needed to use -ljbig sometimes included in pkg-config --static --libs libtiff Another issue is that pkg-config --exists may not be reliable.

XQuartz 2. x only distributed dynamic libraries and not some of the. pc files needed for --exists. Sometimes the name by which the software is known to pkg-config is not what one might expect e. To get a complete list use. Some external software provides a -config command to do a similar job to pkg-config , including. curl-config is for libcurl not curl.

nc-config is for netcdf. Most have an option to use static libraries. These commands indicate what header paths and libraries are needed, but they do not obviate the need to check that the recipes they give actually work. This is especially necessary for platforms which use static linking. If using Autoconf it is good practice to include all the Autoconf sources in the package and required for an Open Source package and tested by R CMD check --as-cran. This will include the file configure.

ac 31 in the top-level directory of the package. If extensions written in m4 are needed, these should be included under the directory tools and included from configure. ac via e. Alternatively, Autoconf can be asked to search all. m4 files in a directory by including something like Next: Configure example , Previous: Configure and cleanup , Up: Configure and cleanup [ Contents ][ Index ].

Sometimes writing your own configure script can be avoided by supplying a file Makevars : also one of the most common uses of a configure script is to make Makevars from Makevars. A Makevars file is a makefile and is used as one of several makefiles by R CMD SHLIB which is called by R CMD INSTALL to compile code in the src directory.

It should be written if at all possible in a portable style, in particular except for Makevars. win and Makevars. ucrt without the use of GNU extensions. When writing a Makevars file for a package you intend to distribute, take care to ensure that it is not specific to your compiler: flags such as -O2 -Wall -pedantic and all other -W flags: for the Oracle compilers these are used to pass arguments to compiler phases are all specific to GCC and compilers such as clang which aim to be options-compatible with it.

Also, do not set variables such as CPPFLAGS , CFLAGS etc. See Customizing package compilation in R Installation and Administration ,. That makefile is included as a Makefile after Makevars[. win] , and the macros it defines can be used in macro assignments and make command lines in the latter. These include. A macro containing the set of libraries need to link Fortran code. A macro containing the BLAS libraries used when building R.

Beware that if it is empty then the R executable will contain all the double-precision and double-complex BLAS routines, but no single-precision nor complex routines. A macro containing the LAPACK libraries and paths where appropriate used when building R. It may point to a dynamic library libRlapack which contains the main double-precision LAPACK routines as well as those double-complex LAPACK routines needed to build R, or it may point to an external LAPACK library, or may be empty if an external BLAS library also contains LAPACK.

See the example later in this section. This can be useful in conjunction with implicit rules to allow other types of source code to be compiled and included in the shared object.

It can also be used to control the set of files which are compiled, either by excluding some files in src or including some files in subdirectories.

Note that Makevars should not normally contain targets, as it is included before the default makefile and make will call the first target, intended to be all in the default makefile. If you really need to circumvent that, use a suitable phony target all before any actual targets in Makevars. needed to ensure that the LAPACK routines find some constants without infinite looping.

The Windows equivalent was. Note that the first target in Makevars will be called, but for back-compatibility it is best named all. If you want to create and then link to a library, say using code in a subdirectory, use something like. Be careful to create all the necessary dependencies, as there is no guarantee that the dependencies of all will be run in a particular order and some of the CRAN build machines use multiple CPUs and parallel makes.

In particular,. but that is not portable. dmake and pmake allow the similar. Note that on Windows it is required that Makevars[. ucrt] does create a DLL: this is needed as it is the only reliable way to ensure that building a DLL succeeded. If you want to use the src directory for some purpose other than building a DLL, use a Makefile.

win or Makefile. ucrt file. win or Makevars. ucrt : this will be used by R CMD build to clean up a copy of the package sources. which asks make to clean in parallel with compiling the code. Not only does this lead to hard-to-debug installation errors, it wipes out all the evidence of any error from a parallel make or not. It is much better to leave cleaning to the end user using the facilities in the previous paragraph. If you want to run R code in Makevars , e. to find configuration information, please do ensure that you use the correct copy of R or Rscript : there might not be one in the path at all, or it might be the wrong version or architecture.

The correct way to do this is via. Environment or make variables can be used to select different macros for and bit code, for example GNU make syntax, allowed on Windows. On Windows there is normally a choice between linking to an import library or directly to a DLL. Where possible, the latter is much more reliable: import libraries are tied to a specific toolchain, and in particular on bit Windows two different conventions have been commonly used. So for example instead of.

where the first and second are conventionally import libraries, the third and fourth often static libraries with. html WIN The fly in the ointment is that the DLL might not be named libxxx. dll , and in fact on bit Windows there is a libxml2.

dll whereas on one build for bit Windows the DLL is called libxml Using import libraries can cover over these differences but can cause equal difficulties. If static libraries are available they can save a lot of problems with run-time finding of DLLs, especially when binary packages are to be distributed and even more when these support both architectures. Where using DLLs is unavoidable we normally arrange via configure.

ucrt to ship them in the same directory as the package DLL. Next: Using pthreads , Previous: Using Makevars , Up: Using Makevars [ Contents ][ Index ]. There is some support for packages which wish to use OpenMP The make macros. If you do use your own checks, make sure that OpenMP support is complete by compiling and linking an OpenMP-using program: on some platforms the runtime library is optional and on others that library depends on other optional libraries.

Some care is needed when compilers are from different families which may use different OpenMP runtimes e. For a package with Fortran code using OpenMP the appropriate lines are. as the C compiler will be used to link the package code.

There are platforms on which this does not work for some OpenMP-using code and installation will fail. So cross-check that e. h header: some compilers but not all include it when OpenMP mode is switched on e. via flag -fopenmp. There is nothing 37 to say what version of OpenMP is supported: version 4.

Apple clang on macOS has no OpenMP support. Rarely, using OpenMP with clang on Linux generates calls in libatomic , resulting in loading messages like. The performance of OpenMP varies substantially between platforms. The Windows implementation has substantial overheads, so is only beneficial if quite substantial tasks are run in parallel. Also, on Windows new threads are started with the default 38 FPU control word, so computations done on OpenMP threads will not make use of extended-precision arithmetic which is the default for the main process.

Many functions in the R API modify internal R data structures and might corrupt these data structures if called simultaneously from multiple threads. Most R API functions can signal errors, which must only happen on the R main thread. Also, external libraries e.

LAPACK may not be thread-safe. Packages are not standard-alone programs, and an R process could contain more than one OpenMP-enabled package as well as other components for example, an optimized BLAS making use of OpenMP. So careful consideration needs to be given to resource usage.

Parallel regions can be nested, although it is common to use only a single thread below the first level. R uses Note that setting environment variables to control OpenMP is implementation-dependent and may need to be done outside the R process or before any use of OpenMP which might be by another process or R itself.

Next: Compiling in sub-directories , Previous: OpenMP support , Up: Using Makevars [ Contents ][ Index ]. There is no direct support for the POSIX threads more commonly known as pthreads : by the time we considered adding it several packages were using it unconditionally so it seems that nowadays it is universally available on POSIX operating systems hence not Windows.

For reasonably recent versions of gcc and clang the correct specification is. For other platforms the specification is. and note that the library name is singular. This is what -pthread does on all known current platforms although earlier versions of OpenBSD used a different library name.

POSIX threads are not normally used on Windows, which has its own native concepts of threads. However, there are two projects implementing pthreads on top of Windows, pthreads-w32 and winpthreads part of the MinGW-w64 project. Whether Windows toolchains implement pthreads is up to the toolchain provider. See also the comments on thread-safety and performance under OpenMP: on all known R platforms OpenMP is implemented via pthreads and the known performance issues are in the latter.

Previous: Using pthreads , Up: Using Makevars [ Contents ][ Index ]. Package authors fairly often want to organize code in sub-directories of src , for example if they are including a separate piece of external software to which this is an R interface. One simple way is simply to set OBJECTS to be all the objects that need to be compiled, including in sub-directories. For example, CRAN package RSiena has.

One problem with that approach is that unless GNU make extensions are used, the source files need to be listed and kept up-to-date. As in the following from CRAN package lossDev :. Where the subdirectory is self-contained code with a suitable makefile, the best approach is something like.

Note the quotes: the macros can contain spaces, e. Others forget the need 42 for position-independent code. Next: Using F9x code , Previous: Using Makevars , Up: Configure and cleanup [ Contents ][ Index ]. The configure. ac file follows: configure is created from this by running autoconf in the top-level package directory containing configure. A user can then be advised to specify the location of the ODBC driver manager files by options like lines broken for easier reading.

R assumes that source files with extension. f95 , but those are not used by R itself so this is not required. The same compiler is used 43 for both fixed-form and free-form Fortran code with different file extensions and possibly different flags.

However, Fortran 95 is widely supported. Intel Fortran has full Fortran support from version Modern versions of Fortran support modules, whereby compiling one source file creates a module file which is then included in others. Module files typically have a. mod extension: they do depend on the compiler used and so should never be included in a package. This creates a dependence which make will not know about and often causes installation with a parallel make to fail.

For example, if file iface. f90 and dmi. Note that it is not portable although some platforms do accept it to define a module of the same name in multiple source files. Next: Using cmake , Previous: Using F9x code , Up: Configure and cleanup [ Contents ][ Index ]. As from R 4. ucrt on Windows should include the line.

Such a check could be. Note that Windows builds prior to R 4. html to see if the features you want to use are widely implemented. Packages often wish to include the sources of other software and compile that for inclusion in their.

so or. dll , which is normally done by including or unpacking the sources in a subdirectory of src , as considered above. Further issues arise when the external software uses another build system such as cmake , principally to ensure that all the settings for compilers, include and load paths etc are made.

This section has already mentioned the need to set at least some of. html manual:cmake-env-variables 7 , but it may be desirable to translate these into native settings such as.

Two approaches have been used. It is often most convenient to build the external software in a directory other than its sources particularly during development when the build directory can be removed between builds rather than attempting to clean the sources — this is illustrated in the first approach.

Next: Writing package vignettes , Previous: Configure and cleanup , Up: Creating R packages [ Contents ][ Index ]. Before using these tools, please check that your package can be installed. R CMD check will inter alia do this, but you may get more detailed error messages doing the install directly. Renviron are used to set environment variables when using these utilities.

win , cleanup. win , configure. ucrt or cleanup. ucrt scripts or a src directory and e. need vignettes built. You may need to set the environment variable TMPDIR to point to a suitable writable directory with a path not containing spaces — use forward slashes for the separators.

Also, the directory needs to be on a case-honouring file system some network-mounted file systems are not. Next: Building package tarballs , Previous: Checking and building packages , Up: Checking and building packages [ Contents ][ Index ].

Using R CMD check , the R package checker, one can test whether source R packages work correctly. It can be run on one or more directories, or compressed package tar archives with extension. tgz ,. It is strongly recommended that the final checks are run on a tar archive prepared by R CMD build.

A warning is given for directory names that look like R package check directories — many packages have been submitted to CRAN containing these. Note that the latter might give false positives in that the symbols might be pulled in with external libraries and could never be called. Of course, released packages should be able to run at least their own examples.

If there is an error 51 in executing the R code in vignette foo. ext , a log file foo. log is created in the check directory. Use R CMD check --help to obtain more information about the usage of the R package checker. A subset of the checking steps can be selected by adding command-line options. Rprofile , by e.

which reports unused local assignments. Not only does this point out computations which are unnecessary because their results are unused, it also can uncover errors. This can give false positives, most commonly because of non-standard evaluation for formulae and because the intention is to return objects in the environment of a function for later use.

Complete checking of a package which contains a file README. You do need to ensure that the package is checked in a suitable locale if it contains non- ASCII characters. Such packages are likely to fail some of the checks in a C locale, and R CMD check will warn if it spots the problem. You should be able to check any package in a UTF-8 locale if one is available.

Beware that although a C locale is rarely used at a console, it may be the default if logging in remotely or for batch jobs. Often R CMD check will need to consult a CRAN repository to check details of uninstalled packages.

Next: Building binary packages , Previous: Checking packages , Up: Checking and building packages [ Contents ][ Index ]. gz files or in binary form. The source form can be installed on all platforms with suitable tools and is the usual form for Unix-like systems; the binary form is platform-specific, and is the more common distribution form for the Windows and macOS platforms. Using R CMD build , the R package builder, one can build R package tarballs from their sources for example, for subsequent release.

Prior to actually building the package in the standard gzipped tar file format, a few diagnostic checks and cleanups are performed. Run-time checks whether the package works correctly should be performed using R CMD check prior to invoking the final build procedure.

To exclude files from being put into the package, one can specify a list of exclude patterns in file. Rbuildignore in the top-level source directory. These patterns should be Perl-like regular expressions see the help for regexp in R for the precise details , one per line, to be matched case-insensitively against the file and directory names relative to the top-level package source directory. In addition, directories from source control systems 54 or from eclipse 55 , directories with names check , chm , or ending.

In addition, same-package tarballs from previous builds and their binary forms will be excluded from the top-level directory, as well as those files in the R , demo and man directories which are flagged by R CMD check as having invalid names.

Use R CMD build --help to obtain more information about the usage of the R package builder. To do so it installs the current package into a temporary library tree, but any dependent packages need to be installed in an available library tree see the Note: at the top of this section. Similarly, if the. If there are any install-time or render-time macros, a.

pdf version of the package manual will be built and installed in the build subdirectory. This allows CRAN or other repositories to display the manual even if they are unable to install the package.

One of the checks that R CMD build runs is for empty source directories. The --resave-data option allows saved images. rda and. RData files in the data directory to be optimized for size. It will also compress tabular files and convert. R files to saved images. Where a non-POSIX file system is in use which does not utilize execute permissions, some care is needed with permissions.

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There was a problem preparing your codespace, please try again. The goal of the NeuroJSON project is to develop human-readable, scalable and future-proof neuroimaging data standards and data sharing services.

JSONLab v2. The major new features include. There have been many major updates added to this release since the previous release v2. The octave-jsonlab package has also been included in the official distributions of Debian Bullseye and Ubuntu The BJData spec was derived from UBJSON spec Draft 12, with the following breaking differences:. To avoid using the new features, one should attach 'UBJSON',1 and 'Endian','B' in the savebj command as.

You are strongly encouraged to convert all pre-v2. jamm files using the new format. JSONLab supports both MATLAB and GNU Octave a free MATLAB clone. As a parser written completely with the native MATLAB language, it is surprisingly fast when reading small-to-moderate sized JSON files MB with simple hierarchical structures, and is heavily optimized for reading JSON files containing large N-D arrays known as the "fast array parser" in loadjson.

JSON JavaScript Object Notation is a highly portable, human-readable and "fat-free" text format to represent complex and hierarchical data, widely used for data-exchange in applications. UBJSON Universal Binary JSON is a binary JSON format, designed to specifically address the limitations of JSON, permitting the storage of binary data with strongly typed data records, resulting in smaller file sizes and fast encoding and decoding.

It is slightly more compact than UBJSON, but is not directly readable compared to UBJSON. We envision that both JSON and its binary counterparts will play important roles for storage, exchange and interoperation of large-scale scientific data among the wide-variety of tools. As container-formats, they offer both the flexibility and generality similar to other more sophisticated formats such as HDF5 , but are significantly simpler with a much greater software ecosystem.

The installation of JSONLab is no different from installing any other MATLAB toolbox. If you want to add this path permanently, you can type pathtool , browse to the JSONLab root folder and add to the list, then click "Save". If you use MATLAB in a shared environment such as a Linux server, the best way to add path is to type. MATLAB will execute this file every time it starts. octaverc , where ~ is your home directory.

ZMat can also compress large arrays that MATLAB's Java-based compression API does not support. JSONLab has been available as an official Fedora package since You may install it directly using the below command. JSONLab is currently available on Ubuntu To install, you may run. The detailed help information can be found in the Contents. m file. In JSONLab 2. Under the examples folder, you can find several scripts to demonstrate the basic utilities of JSONLab.

m script, you will see the conversions from MATLAB data structure to JSON text and backward. m is for testing savemsgpack and loadmsgpack.

Please run these examples and understand how JSONLab works before you use it to process your data. Under the test folder, you can find a script to test individual data types and inputs using various encoders and decoders.

This unit testing script also serves as a specification validator to the JSONLab functions and ensure that the outputs are compliant to the underlying specifications. Starting from JSONLab v2. The file size is comparable can be smaller if use lzma compression to. mat files. This feature is currently experimental.

Therefore, JSONLab-created JSON files. jnirs etc can be readily read and written by nearly all existing JSON parsers, including the built-in json module parser in Python. jamm etc. Using binary JData files are expected to produce much smaller file sizes and faster parsing, while maintaining excellent portability and generality. To install these modules on Python 2. After the installation is done, one can then install the jdata and bjdata modules by. To install these modules for Python 3.

x, please replace pip by pip3. If one prefers to install these modules globally for all users, simply execute the above commands using. The above modules require built-in Python modules json and NumPy numpy. Once the necessary modules are installed, one can type python or python3 , and run. where jd. loadt function loads a text-based JSON file, performs JData decoding and converts the enclosed data into Python dict , list and numpy objects.

Similarly, jd. One can directly call jd. Similarly, the jd. savet , jd. saveb and jd. JSONLab has several known limitations. We are striving to make it more general and robust. Hopefully in a few future releases, the limitations become less. JSONLab is an open-source project. This means you can not only use it and modify it as you wish, but also you can contribute your changes back to JSONLab so that everyone else can enjoy the improvement. For anyone who want to contribute, please download JSONLab source code from its source code repositories by using the following command:.

Sometimes, you may find it is necessary to modify JSONLab to achieve your goals, or attempt to modify JSONLab functions to fix a bug that you have encountered. If you are happy with your changes and willing to share those changes to the upstream author, you are recommended to create a pull-request on github.

To create a pull-request, you first need to "fork" jsonlab on Github by clicking on the "fork" button on top-right of JSONLab's github page. Once you forked jsonlab to your own directory, you should then implement the changes in your own fork. Then type in the description of the changes. You are responsible to format the code updates using the same convention tab-width: 8, indentation: 4 spaces as the upstream code.

We appreciate any suggestions and feedbacks from you. Please use the following mailing list to report any questions you may have regarding JSONLab:. The loadjson. m function was significantly modified from the earlier parsers BSD 3-clause licensed written by the below authors. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:. Skip to content. Star BSDClause license.

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Thinking about your own personal finances—would you say that you and your family are financially better off, worse off, or just about the same as a year ago? m, gzipencode. Why, ones like the "agreement between Activision Blizzard and Sony," that places "restrictions on the ability of Activision Blizzard to place COD titles on Game Pass for a number of years". Sweave, provided by the R distribution, is the default engine. if library foo was not found with the desired functionality.

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