Release 1.3.2
* Added Border, Fill, Pattern and Gradient formatting to chart data labels and
chart custom data labels. See :ref:`chart_series_option_data_labels` and
:ref:`chart_series_option_custom_data_labels`.
Release v1.31.0
Core
The following new xDS functionality is added in this release:
Requests matching based on path (prefix, full path and safe regex) and headers.
Requests routing to multiple clusters based on weights.
The features supported in a given release are documented here.
Other changes:
Remove MAX_EPOLL_EVENTS_HANDLED_EACH_POLL_CALL to ensure timely processing of events.
Include the target name in top-level DNS error messages.
Remove xds-experimental URI scheme.
fix memory leak of grpc_resource_user_quota.
Store ref to the ExternalConnectivityWatcher in external_watchers_ map.
Update grpclb configuration with field "service_name".
Fix possible deadlock in RemoveExternalConnectivityWatcher.
Enable TLS 1.3 in the C-core and all wrapped languages.
Add message-size check before message decompression with ordering change.
Fix race condition caused by simultaneous updates on SSL server handshaker.
Add missing reset for ping clocks to avoid mistakenly sending GOAWAY frames due to 'too_many_pings'.
C++
Simplify makefile: Get rid of "install" rules with pure make, recommend cmake and bazel instead.
Replaced grpc::string with std::string.
Fix wrong version in gRPCConfigVersion.cmake and grpc++*.pc.
Python
[Aio] Support tuple and aio.Metadata interaction.
[Aio] Allows poller to bind to ephemeral loops in multiple threads.
[Aio] Hide init_grpc_aio and guard async API outside of AsyncIO context.
[Aio] Implement methods to access auth context and peer info.
Add protobuf as an "extras" dependency to grpcio package.
[Aio] Use Metadata type.
Avoid attribute error in del of _ChannelCallState.
Default wait_for_ready to True in simple stubs.
Propagate contextvars to auxiliary threads.
Simplify channel credentials in simple stubs.
1.10.0
Features
allow to use 'six.moves.collections_abc.Mapping' in 'client_options.from_dict()'
Build universal wheels
discovery supports retries
Documentation
consolidating and updating the Contribution Guide
1.20.1
Bug Fixes
reduce refresh clock skew to 10 seconds
set Content-Type header in the request to signBlob API to avoid Invalid JSON payload error
1.20.0
Features
Add debug logging that can help with diagnosing auth lib. path
Show the transport exception that happened for GCE Metadata
packaging: add support for Python 3.8
Comprehensive open-source toolbox for analysing Spatial Point
Patterns. Focused mainly on two-dimensional point patterns, including
multitype/marked points, in any spatial region. Also supports
three-dimensional point patterns, space-time point patterns in any
number of dimensions, point patterns on a linear network, and patterns
of other geometrical objects. Supports spatial covariate data such as
pixel images. Contains over 2000 functions for plotting spatial data,
exploratory data analysis, model-fitting, simulation, spatial
sampling, model diagnostics, and formal inference. Many data types and
exploratory methods are supported. Formal hypothesis tests of random
pattern and tests for covariate effects are also supported. Parametric
models can be fitted to point pattern data using the functions ppm(),
kppm(), slrm(), dppm() similar to glm(). Types of models include
Poisson, Gibbs and Cox point processes, Neyman-Scott cluster
processes, and determinantal point processes. Models may involve
dependence on covariates, inter-point interaction, cluster formation
and dependence on marks. Models are fitted by maximum likelihood,
logistic regression, minimum contrast, and composite likelihood
methods. A model can be fitted to a list of point patterns (replicated
point pattern data) using the function mppm(). The model can include
random effects and fixed effects depending on the experimental design,
in addition to all the features listed above. Fitted point process
models can be simulated, automatically. Formal hypothesis tests of a
fitted model are supported along with basic tools for model selection.
This package provides an easy and simple way to read, write and
display bitmap images stored in the PNG format. It can read and write
both files and in-memory raw vectors.
Automates the process of creating a scale bar and north arrow in any
package that uses base graphics to plot in R. Bounding box tools help
find and manipulate extents. Finally, there is a function to automate
the process of setting margins, plotting the map, scale bar, and north
arrow, and resetting graphic parameters upon completion.
Interface to Geometry Engine - Open Source ('GEOS') using the C 'API'
for topology operations on geometries. The 'GEOS' library is external
to the package, and, when installing the package from source, must be
correctly installed first. Windows and Mac Intel OS X binaries are
provided on 'CRAN'. ('rgeos' >= 0.5-1): Up to and including 'GEOS'
3.7.1, topological operations succeeded with some invalid geometries
for which the same operations fail from and including 'GEOS' 3.7.2.
The 'checkValidity=' argument defaults and structure have been
changed, from default FALSE to integer default '0L' for 'GEOS' < 3.7.2
(no check), '1L' 'GEOS' >= 3.7.2 (check and warn). A value of '2L' is
also provided that may be used, assigned globally using
'set_RGEOS_CheckValidity(2L)', or locally using the 'checkValidity=2L'
argument, to attempt zero-width buffer repair if invalid geometries
are found. The previous default (FALSE, now '0L') is fastest and used
for 'GEOS' < 3.7.2, but will not warn users of possible problems
before the failure of topological operations that previously
succeeded.
A system for writing hierarchical statistical models largely
compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate
models and do basic R-style math, and compiling both models and
nimbleFunctions via custom- generated C++. 'NIMBLE' includes default
methods for MCMC, particle filtering, Monte Carlo Expectation
Maximization, and some other tools. The nimbleFunction system makes it
easy to do things like implement new MCMC samplers from R, customize
the assignment of samplers to different parts of a model from R, and
compile the new samplers automatically via C++ alongside the samplers
'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by
making it extensible: New distributions and functions can be added,
including as calls to external compiled code. Although most people
think of MCMC as the main goal of the 'BUGS'/'JAGS' language for
writing models, one can use 'NIMBLE' for writing arbitrary other kinds
of model-generic algorithms as well. A full User Manual is available
at <https://r-nimble.org>.
LearnBayes contains a collection of functions helpful in learning the
basic tenets of Bayesian statistical inference. It contains functions
for summarizing basic one and two parameter posterior distributions
and predictive distributions. It contains MCMC algorithms for
summarizing posterior distributions defined by the user. It also
contains functions for regression models, hierarchical models,
Bayesian tests, and illustrations of Gibbs sampling.
A general purpose toolbox for personality, psychometric theory and
experimental psychology. Functions are primarily for multivariate
analysis and scale construction using factor analysis, principal
component analysis, cluster analysis and reliability analysis,
although others provide basic descriptive statistics. Item Response
Theory is done using factor analysis of tetrachoric and polychoric
correlations. Functions for analyzing data at multiple levels include
within and between group statistics, including correlations and factor
analysis. Functions for simulating and testing particular item and
test structures are included. Several functions serve as a useful
front end for structural equation modeling. Graphical displays of
path diagrams, factor analysis and structural equation models are
created using basic graphics. Some of the functions are written to
support a book on psychometric theory as well as publications in
personality research. For more information, see the
personality-project.org/r web page.
Functions are provided for computing the density and the distribution
function of multivariate normal and "t" random variables, and for
generating random vectors sampled from these distributions.
Probabilities are computed via non-Monte Carlo methods; different
routines are used in the case d=1, d=2, d>2, if d denotes the number
of dimensions.
The tensor product of two arrays is notionally an outer product of the
arrays collapsed in specific extents by summing along the appropriate
diagonals.
Functions to work with date-times and time-spans: fast and user
friendly parsing of date-time data, extraction and updating of
components of a date-time (years, months, days, hours, minutes, and
seconds), algebraic manipulation on date-time and time-span objects.
The 'lubridate' package has a consistent and memorable syntax that
makes working with dates easy and fun. Parts of the 'CCTZ' source
code, released under the Apache 2.0 License, are included in this
package. See <https://github.com/google/cctz> for more details.
A wrapper for 'libcurl' <http://curl.haxx.se/libcurl/> Provides
functions to allow one to compose general HTTP requests and provides
convenient functions to fetch URIs, get & post forms, etc. and process
the results returned by the Web server. This provides a great deal of
control over the HTTP/FTP/... connection and the form of the request
while providing a higher-level interface than is available just using
R socket connections. Additionally, the underlying implementation is
robust and extensive, supporting FTP/FTPS/TFTP (uploads and
downloads), SSL/HTTPS, telnet, dict, ldap, and also supports cookies,
redirects, authentication, etc.
