Tag: tutorial

Rcpp Workshop in San Francisco Oct 8th

 Rcpp Workshop in San Francisco  Oct 8th 

Following the successful one-day master class on Rcpp preceding this year’s R/Finance conference, a full-day master class on Rcpp and related topics which will be held on Saturday, October 8, in San Francisco.

Join Dirk Eddelbuettel for six hours of detailed and hands-on instructions and discussions aroundRcpp, inline,  RInside, RcppArmadillo, RcppGSL, RcppEigen and other packages—in an intimate small-group setting.

The full-day format allows combining an introductory morning session with a more advanced afternoon session while leaving room for sufficient breaks. We plan on having about six hours of instructions, a one-hour lunch break and two half-hour coffee breaks (and lunch and refreshments will be provided).

Morning session: “A Hands-on Introduction to R and C++”

The morning session will provide a practical introduction to the Rcpp package (and other related packages).  The focus will be on simple and straightforward applications of Rcpp in order to extend R and/or to significantly accelerate the execution of simple functions.

The tutorial will cover the inline package which permits embedding of self-contained C, C++ or FORTRAN code in R scripts. We will also discuss  RInside, to easily embed the R engine code in C++ applications, as well as standard Rcpp extension packages such as RcppArmadillo and RcppEigen for linear algebra (via highly expressive templated C++ libraries) and RcppGSL.

Afternoon session: “Advanced R and C++ Topics”

The afternoon tutorial will provide a hands-on introduction to more advanced Rcpp features. It will cover topics such as writing packages that use Rcpp, how Rcpp modules and the new R ReferenceClasses interact, and how Rcpp sugar lets us write C++ code that is often as expressive as R code. Another possible topic, time permitting, may be writing glue code to extend Rcpp to other C++ projects.

We also expect to leave some time to discuss problems brought by the class participants.

October 8, 2011 – San Franciso

AMA Executive Conference Center
@ the Marriott Hotel
55 4th Street, 2nd Level
San Francisco, CA 94103
Tel.             415-442-6770

Register Now!

Instructor Bio

Dirk Eddelbuettel Dirk E has been contributing packages to CRAN for nearly a decade. Among these are RQuantLib, digest, littler, random, RPostgreSQL, as well the Rcpp family of packages comprising Rcpp, RInside, RcppClassic, RcppExamples, RcppDE, RcppArmadillo and RcppEigen. He maintains the CRAN Task Views for Finance as well as High-Performance Computing, and is a founding co-organiser of the annual R / Finance conferences in Chicago. He has Ph.D. in Financial Econometrics from EHESS (Paris), and works in Chicago as a Quantitative Strategist.

Google Plus API- statistical text mining anyone

For the past year and two I have noticed a lot of statistical analysis using #rstats /R on unstructured text generated in real time by the social network Twitter. From an analytic point of view , Google Plus is an interesting social network , as it is a social network that is new and arrived after the analytic tools are relatively refined. It is thus an interesting use case for evolution of people behavior measured globally AFTER analytic tools in text mining are evolved and we can thus measure how people behave and that behavior varies as the social network and its user interface evolves.

And it would also be  a nice benchmark to do sentiment analysis across multiple social networks.

Some interesting use cases of using Twitter that have been used in R.

  • Using R to search Twitter for analysis
http://www.franklincenterhq.org/2429/using-r-to-search-twitter-for-analysis/
  • Text Data Mining With Twitter And R
  • TWITTER FROM R… SURE, WHY NOT!
  • A package called TwitteR
  • slides from my R tutorial on Twitter text mining #rstats
  • Generating graphs of retweets and @-messages on Twitter using R and Gephi
But with Google Plus API now active

The Console lets you see and manage the following project information:

  • Activated APIs – Activate one or more APIs to enable traffic monitoring, filtering, and billing, and API-specific pages for your project. Read more about activating APIs here.
  • Traffic information – The Console reports traffic information for each activated API. Additionally, you can cap or filter usage by API. Read more about traffic reporting and request filtering here.
  • Billing information – When you activate billing, your activated APIs can exceed the courtesy usage quota. Usage fees are billed to the Google Checkout account that you specify. Read more about billing here.
  • Project keys – Each project is identified by either an API key or an OAuth 2.0 token. Use this key/token in your API requests to identify the project, in order to record usage data, enforce your filtering restrictions, and bill usage to the proper project. You can use the Console to generate or revoke API keys or OAuth 2.0 certificates to use in your application. Read more about keys here.
  • Team members – You can specify additional members with read, write, or ownership access to this project’s Console page. Read more about team members here.
Google+ API Courtesy limit: 1,000 queries/day

Effective limits:

API Per-User Limit Used Courtesy Limit
Google+ API 5.0 requests/second/user 0% 1,000 queries/day
API Calls
Most of the Google+ API follows a RESTful API design, meaning that you use standard HTTP methods to retrieve and manipulate resources. For example, to get the profile of a user, you might send an HTTP request like:

GET https://www.googleapis.com/plus/v1/people/userId

Common Parameters

Different API methods require parameters to be passed either as part of the URL path or as query parameters. Additionally, there are a few parameters that are common to all API endpoints. These are all passed as optional query parameters.

Parameter Name

Value

Description

callback

string

Specifies a JavaScript function that will be passed the response data for using the API with JSONP.

fields

string

Selector specifying which fields to include in a partial response.

key

string

API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.

access_token

string

OAuth 2.0 token for the current user. Learn more about OAuth.

prettyPrint

boolean

If set to “true”, data output will include line breaks and indentation to make it more readable. If set to “false”, unnecessary whitespace is removed, reducing the size of the response. Defaults to “true”.

userIp

string

Identifies the IP address of the end user for whom the API call is being made. This allows per-user quotas to be enforced when calling the API from a server-side application. Learn more about Capping Usage.

Data Formats

Resources in the Google+ API are represented using JSON data formats. For example, retrieving a user’s profile may result in a response like:

{
  "kind": "plus#person",
  "id": "118051310819094153327",
  "displayName": "Chirag Shah",
  "url": "https://plus.google.com/118051310819094153327",
  "image": {
    "url": "https://lh5.googleusercontent.com/-XnZDEoiF09Y/AAAAAAAAAAI/AAAAAAAAYCI/7fow4a2UTMU/photo.jpg"
  }
}

Common Properties

While each type of resource will have its own unique representation, there are a number of common properties that are found in almost all resource representations.

Property Name

Value

Description

displayName

string

This is the name of the resource, suitable for displaying to a user.

id

string

This property uniquely identifies a resource. Every resource of a given kind will have a unique id. Even though an id may sometimes look like a number, it should always be treated as a string.

kind

string

This identifies what kind of resource a JSON object represents. This is particularly useful when programmatically determining how to parse an unknown object.

url

string

This is the primary URL, or permalink, for the resource.

Pagination

In requests that can respond with potentially large collections, such as Activities list, each response contains a limited number of items, set by maxResults(default: 20). Each response also contains a nextPageToken property. To obtain the next page of items, you pass this value of nextPageToken to the pageTokenproperty of the next request. Repeat this process to page through the full collection.

For example, calling Activities list returns a response with nextPageToken:

{
  "kind": "plus#activityFeed",
  "title": "Plus Public Activities Feed",
  "nextPageToken": "CKaEL",
  "items": [
    {
      "kind": "plus#activity",
      "id": "123456789",
      ...
    },
    ...
  ]
  ...
}

To get the next page of activities, pass the value of this token in with your next Activities list request:

https://www.googleapis.com/plus/v1/people/me/activities/public?pageToken=CKaEL

As before, the response to this request includes nextPageToken, which you can pass in to get the next page of results. You can continue this cycle to get new pages — for the last page, “nextPageToken” will be absent.

 

it would be interesting the first wave of analysis on this new social network and see if it is any different from others, if at all.
After all, an API is only as good as the analysis and applications  that can be done on the data it provides

 

Amazon Ec2 goes Red Hat

message from Amazing Amazon’s cloud team- this will also help for #rstats users given that revolution Analytics full versions on RHEL.

—————————————————-

on-demand instances of Amazon EC2 running Red Hat Enterprise Linux (RHEL) for as little as $0.145 per instance hour. The offering combines the cost-effectiveness, scalability and flexibility of running in Amazon EC2 with the proven reliability of Red Hat Enterprise Linux.

Highlights of the offering include:

  • Support is included through subscription to AWS Premium Support with back-line support by Red Hat
  • Ongoing maintenance, including security patches and bug fixes, via update repositories available in all Amazon EC2 regions
  • Amazon EC2 running RHEL currently supports RHEL 5.5, RHEL 5.6, RHEL 6.0 and RHEL 6.1 in both 32 bit and 64 bit formats, and is available in all Regions.
  • Customers who already own Red Hat licenses will continue to be able to use those licenses at no additional charge.
  • Like all services offered by AWS, Amazon EC2 running Red Hat Enterprise Linux offers a low-cost, pay-as-you-go model with no long-term commitments and no minimum fees.

For more information, please visit the Amazon EC2 Red Hat Enterprise Linux page.

which is

Amazon EC2 Running Red Hat Enterprise Linux

Amazon EC2 running Red Hat Enterprise Linux provides a dependable platform to deploy a broad range of applications. By running RHEL on EC2, you can leverage the cost effectiveness, scalability and flexibility of Amazon EC2, the proven reliability of Red Hat Enterprise Linux, and AWS premium support with back-line support from Red Hat.. Red Hat Enterprise Linux on EC2 is available in versions 5.5, 5.6, 6.0, and 6.1, both in 32-bit and 64-bit architectures.

Amazon EC2 running Red Hat Enterprise Linux provides seamless integration with existing Amazon EC2 features including Amazon Elastic Block Store (EBS), Amazon CloudWatch, Elastic-Load Balancing, and Elastic IPs. Red Hat Enterprise Linux instances are available in multiple Availability Zones in all Regions.

Sign Up

Pricing

Pay only for what you use with no long-term commitments and no minimum fee.

On-Demand Instances

On-Demand Instances let you pay for compute capacity by the hour with no long-term commitments.

Region:US – N. VirginiaUS – N. CaliforniaEU – IrelandAPAC – SingaporeAPAC – Tokyo
Standard Instances Red Hat Enterprise Linux
Small (Default) $0.145 per hour
Large $0.40 per hour
Extra Large $0.74 per hour
Micro Instances Red Hat Enterprise Linux
Micro $0.08 per hour
High-Memory Instances Red Hat Enterprise Linux
Extra Large $0.56 per hour
Double Extra Large $1.06 per hour
Quadruple Extra Large $2.10 per hour
High-CPU Instances Red Hat Enterprise Linux
Medium $0.23 per hour
Extra Large $0.78 per hour
Cluster Compute Instances Red Hat Enterprise Linux
Quadruple Extra Large $1.70 per hour
Cluster GPU Instances Red Hat Enterprise Linux
Quadruple Extra Large $2.20 per hour

Pricing is per instance-hour consumed for each instance type. Partial instance-hours consumed are billed as full hours.

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and

Available Instance Types

Standard Instances

Instances of this family are well suited for most applications.

Small Instance – default*

1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small

Large Instance

7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large

Extra Large Instance

15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge

Micro Instances

Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.

Micro Instance

613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro

High-Memory Instances

Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.

High-Memory Extra Large Instance

17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge

High-Memory Double Extra Large Instance

34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge

High-Memory Quadruple Extra Large Instance

68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge

High-CPU Instances

Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.

High-CPU Medium Instance

1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium

High-CPU Extra Large Instance

7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge

Cluster Compute Instances

Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.

Cluster Compute Quadruple Extra Large Instance

23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge

Cluster GPU Instances

Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn more about use of this instance type for HPC applications.

Cluster GPU Quadruple Extra Large Instance

22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge

 

Getting Started

To get started using Red Hat Enterprise Linux on Amazon EC2, perform the following steps:

  • Open and log into the AWS Management Console
  • Click on Launch Instance from the EC2 Dashboard
  • Select the Red Hat Enterprise Linux AMI from the QuickStart tab
  • Specify additional details of your instance and click Launch
  • Additional details can be found on each AMI’s Catalog Entry page

The AWS Management Console is an easy tool to start and manage your instances. If you are looking for more details on launching an instance, a quick video tutorial on how to use Amazon EC2 with the AWS Management Console can be found here .
A full list of Red Hat Enterprise Linux AMIs can be found in the AWS AMI Catalog.

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Support

All customers running Red Hat Enterprise Linux on EC2 will receive access to repository updates from Red Hat. Moreover, AWS Premium support customers can contact AWS to get access to a support structure from both Amazon and Red Hat.

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Resources

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About Red Hat

Red Hat, the world’s leading open source solutions provider, is headquartered in Raleigh, NC with over 50 satellite offices spanning the globe. Red Hat provides high-quality, low-cost technology with its operating system platform, Red Hat Enterprise Linux, together with applications, management and Services Oriented Architecture (SOA) solutions, including the JBoss Enterprise Middleware Suite. Red Hat also offers support, training and consulting services to its customers worldwide.

 

also from Revolution Analytics- in case you want to #rstats in the cloud and thus kill all that talk of RAM dependency, slow R than other softwares (just increase the RAM above in the instances to keep it simple)

,or Revolution not being open enough

http://www.revolutionanalytics.com/downloads/gpl-sources.php

GPL SOURCES

Revolution Analytics uses an Open-Core Licensing model. We provide open- source R bundled with proprietary modules from Revolution Analytics that provide additional functionality for our users. Open-source R is distributed under the GNU Public License (version 2), and we make our software available under a commercial license.

Revolution Analytics respects the importance of open source licenses and has contributed code to the open source R project and will continue to do so. We have carefully reviewed our compliance with GPLv2 and have worked with Mark Radcliffe of DLA Piper, the outside General Legal Counsel of the Open Source Initiative, to ensure that we fully comply with the obligations of the GPLv2.

For our Revolution R distribution, we may make some minor modifications to the R sources (the ChangeLog file lists all changes made). You can download these modified sources of open-source R under the terms of the GPLv2, using either the links below or those in the email sent to you when you download a specific version of Revolution R.

Download GPL Sources

Product Version Platform Modified R Sources
Revolution R Community 3.2 Windows R 2.10.1
Revolution R Community 3.2 MacOS R 2.10.1
Revolution R Enterprise 3.1.1 RHEL R 2.9.2
Revolution R Enterprise 4.0 Windows R 2.11.1
Revolution R Enterprise 4.0.1 RHEL R 2.11.1
Revolution R Enterprise 4.1.0 Windows R 2.11.1
Revolution R Enterprise 4.2 Windows R 2.11.1
Revolution R Enterprise 4.2 RHEL R 2.11.1
Revolution R Enterprise 4.3 Windows & RHEL R 2.12.2

 

 

 

Revolution releases R Windows for Academics for free

Logo for R
Image via Wikipedia

Based on the official email from them, God bless the merry coders at Revo-

Revolution Analytics has just released Revolution R Enterprise 4.3 for 32-bit and 64-bit Windows, a significant step forward in enterprise data analytics.  It features an updated RevoScaleR package for scalable, fast (multicore), and extensible data analysis with R. Revolution R Enterprise 4.3 for Windows also provides R 2.12.2, and includes an enhanced R Productivity Environment (RPE), a full-featured integrated development environment with visual debugging capabilities. Also available is an updated Windows release of our deployment server solution, RevoDeployR 1.2, designed to help you deliver R analytics via the Web.

As a registered user of the Academic version of Revolution R Enterprise for Windows, you can take advantage of these improvements by downloading and installing Revolution R Enterprise 4.3 today. You can install Revolution R Enterprise 4.3 side-by-side with your existing Revolution R Enterprise installations; there is no need to uninstall previous versions.

 

Top ten business analytics graphs Bar Charts (3/10)

Bar Charts and Histograms-Bar Charts are one of the most widely used types of Business Charts. Even the ever popular histograms are  special cases of bar charts (but showing frequencies). Histograms are the not the same as bar charts, they are simply bar charts of frequencies.

Basically a bar chart shows rectangular bars with length proportional to the quantities being described. It helps to see relative quantities between various category types.

The barplot() command is used for making Bar Plots, while hist() is used for histograms. You can also use the plot() command with type=h to create histograms-The official R manual also suggests that Dot plots using dotchart () are a reasonable substitute for bar plots.
A very simple easy to understand tutorial for basic bar plots is at http://msenux.redwoods.edu/math/R/barplot.php

The difference between the three main functions that can be used for these charts are shown below-

> VADeaths
Rural Male Rural Female Urban Male Urban Female
50-54       11.7          8.7       15.4          8.4
55-59       18.1         11.7       24.3         13.6
60-64       26.9         20.3       37.0         19.3
65-69       41.0         30.9       54.6         35.1
70-74       66.0         54.3       71.1         50.0

> plot(VADeaths,type=”h”)


> dotchart(VADeaths)

Youtube's variance in interface/s for sharing

Youtube seems to have a different  interface for sharing a channel, a playlist or an individual song. Also it seems to be missing out on revenue from Itunes (or maybe it isnt). and it seems to promoting Facebook and Twitter to the expense of other social media sharing buttons which can be only seen when you click share more (or maybe the buttons/social media channels change based on sharing activity analytics 🙂 )

on a slightly different note read my techie tutorial on boosting your youtube channel views

https://decisionstats.com/2010/09/10/creating-an-anonymous-bot/

Creating an Anonymous Bot

 

See the following interface snapshots/views-

youtube song share expanded
youtube song share expanded

 

youtube song share
youtube song share default
youtube playlist share
youtube playlist share
utube channel share
youtube channel share
Related Articles

Whats new in the latest version of R

Figure 2: The argument φ and modulus r locate ...
Image via Wikipedia
CHANGES IN R VERSION 2.12.2: http://cran.r-project.org/src/base/NEWS

  SIGNIFICANT USER-VISIBLE CHANGES:

    • Complex arithmetic (notably z^n for complex z and integer n) gave
      incorrect results since R 2.10.0 on platforms without C99 complex
      support.  This and some lesser issues in trignometric functions
      have been corrected.

      Such platforms were rare (we know of Cygwin and FreeBSD).
      However, because of new compiler optimizations in the way complex
      arguments are handled, the same code was selected on x86_64 Linux
      with gcc 4.5.x at the default -O2 optimization (but not at -O).

    • There is a workaround for crashes seen with several packages on
      systems using zlib 1.2.5: see the INSTALLATION section.

  NEW FEATURES:

    • PCRE has been updated to 8.12 (two bug-fix releases since 8.10).

    • rep(), seq(), seq.int() and seq_len() report more often when the
      first element is taken of an argument of incorrect length.

    • The Cocoa back-end for the quartz() graphics device on Mac OS X
      provides a way to disable event loop processing temporarily
      (useful, e.g., for forked instances of R).

    • kernel()'s default for m was not appropriate if coef was a set of
      coefficients.  (Reported by Pierre Chausse.)

    • bug.report() has been updated for the current R bug tracker,
      which does not accept emailed submissions.

    • R CMD check now checks for the correct use of $(LAPACK_LIBS) (as
      well as $(BLAS_LIBS)), since several CRAN recent submissions have
      ignored ‘Writing R Extensions’.

  INSTALLATION:

    • The zlib sources in the distribution are now built with all
      symbols remapped: this is intended to avoid problems seen with
      packages such as XML and rggobi which link to zlib.so.1 on
      systems using zlib 1.2.5.

    • The default for FFLAGS and FCFLAGS with gfortran on x86_64 Linux
      has been changed back to -g -O2: however, setting -g -O may still
      be needed for gfortran 4.3.x.

  PACKAGE INSTALLATION:

    • A LazyDataCompression field in the DESCRIPTION file will be used
      to set the value for the --data-compress option of R CMD INSTALL.

    • Files R/sysdata.rda of more than 1Mb are now stored in the
      lazyload daabase using xz compression: this for example halves
      the installed size of package Imap.

    • R CMD INSTALL now ensures that directories installed from inst
      have search permission for everyone.

      It no longer installs files inst/doc/Rplots.ps and
      inst/doc/Rplots.pdf.  These are almost certainly left-overs from
      Sweave runs, and are often large.

  DEPRECATED & DEFUNCT:

    • The ‘experimental’ alternative specification of a name space via
      .Export() etc is now deprecated.

    • zip.file.extract() is now deprecated.

    • Zip-ing data sets in packages (and hence R CMD INSTALL
      --use-zip-data and the ZipData: yes field in a DESCRIPTION file)
      is deprecated: using efficiently compressed .rda images and
      lazy-loading of data has superseded it.

  BUG FIXES:

    • identical() could in rare cases generate a warning about
      non-pairlist attributes on CHARSXPs.  As these are used for
      internal purposes, the attribute check should be skipped.
      (Reported by Niels Richard Hansen).

    • If the filename extension (usually .Rnw) was not included in a
      call to Sweave(), source references would not work properly and
      the keep.source option failed.  (PR#14459)

    • format.data.frame() now keeps zero character column names.

    • pretty(x) no longer raises an error when x contains solely
      non-finite values. (PR#14468)

    • The plot.TukeyHSD() function now uses a line width of 0.5 for its
      reference lines rather than lwd = 0 (which caused problems for
      some PDF and PostScript viewers).

    • The big.mark argument to prettyNum(), format(), etc. was inserted
      reversed if it was more than one character long.

    • R CMD check failed to check the filenames under man for Windows'
      reserved names.

    • The "Date" and "POSIXt" methods for seq() could overshoot when to
      was supplied and by was specified in months or years.

    • The internal method of untar() now restores hard links as file
      copies rather than symbolic links (which did not work for
      cross-directory links).

    • unzip() did not handle zip files which contained filepaths with
      two or more leading directories which were not in the zipfile and
      did not already exist.  (It is unclear if such zipfiles are valid
      and the third-party C code used did not support them, but
      PR#14462 created one.)

    • combn(n, m) now behaves more regularly for the border case m = 0.
      (PR#14473)

    • The rendering of numbers in plotmath expressions (e.g.
      expression(10^2)) used the current settings for conversion to
      strings rather than setting the defaults, and so could be
      affected by what has been done before.  (PR#14477)

    • The methods of napredict() and naresid() for na.action =
      na.exclude fits did not work correctly in the very rare event
      that every case had been omitted in the fit.  (Reported by Simon
      Wood.)

    • weighted.residuals(drop0=TRUE) returned a vector when the
      residuals were a matrix (e.g. those of class "mlm").  (Reported
      by Bill Dunlap.)

    • Package HTML index files /html/00Index.html were generated
      with a stylesheet reference that was not correct for static
      browsing in libraries.

    • ccf(na.action = na.pass) was not implemented.

    • The parser accepted some incorrect numeric constants, e.g. 20x2.
      (Reported by Olaf Mersmann.)

    • format(*, zero.print) did not always replace the full zero parts.

    • Fixes for subsetting or subassignment of "raster" objects when
      not both i and j are specified.

    • R CMD INSTALL was not always respecting the ZipData: yes field of
      a DESCRIPTION file (although this is frequently incorrectly
      specified for packages with no data or which specify lazy-loading
      of data).

      R CMD INSTALL --use-zip-data was incorrectly implemented as
      --use-zipdata since R 2.9.0.

    • source(file, echo=TRUE) could fail if the file contained #line
      directives.  It now recovers more gracefully, but may still
      display the wrong line if the directive gives incorrect
      information.

    • atan(1i) returned NaN+Infi (rather than 0+Infi) on platforms
      without C99 complex support.

    • library() failed to cache S4 metadata (unlike loadNamespace())
      causing failures in S4-using packages without a namespace (e.g.
      those using reference classes).

    • The function qlogis(lp, log.p=TRUE) no longer prematurely
      overflows to Inf when exp(lp) is close to 1.

    • Updating S4 methods for a group generic function requires
      resetting the methods tables for the members of the group (patch
      contributed by Martin Morgan).

    • In some circumstances (including for package XML), R CMD INSTALL
      installed version-control directories from source packages.

    • Added PROTECT calls to some constructed expressions used in C
      level eval calls.

    • utils:::create.post() (used by bug.report() and help.request())
      failed to quote arguments to the mailer, and so often failed.

    • bug.report() was naive about how to extract maintainer email
      addresses from package descriptions, so would often try mailing
      to incorrect addresses.

    • debugger() could fail to read the environment of a call to a
      function with a ... argument.  (Reported by Charlie Roosen.)    • prettyNum(c(1i, NA), drop0=TRUE) or str(NA_complex_) now work
      correctly.

 

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