See: Description
| Package | Description |
|---|---|
| java.applet |
Provides the classes necessary to create an applet and the classes an applet
uses to communicate with its applet context.
|
| java.awt |
Contains all of the classes for creating user
interfaces and for painting graphics and images.
|
| java.awt.color |
Provides classes for color spaces.
|
| java.awt.datatransfer |
Provides interfaces and classes for transferring data
between and within applications.
|
| java.awt.dnd |
Drag and Drop is a direct manipulation gesture found in many Graphical
User Interface systems that provides a mechanism to transfer
information between two entities logically associated with presentation
elements in the GUI.
|
| java.awt.event |
Provides interfaces and classes for dealing with different
types of events fired by AWT components.
|
| java.awt.font |
Provides classes and interface relating to fonts.
|
| java.awt.geom |
Provides the Java 2D classes for defining and performing operations
on objects related to two-dimensional geometry.
|
| java.awt.im |
Provides classes and interfaces for the input method framework.
|
| java.awt.im.spi |
Provides interfaces that enable the development of input methods
that can be used with any Java runtime environment.
|
| java.awt.image |
Provides classes for creating and modifying images.
|
| java.awt.image.renderable |
Provides classes and interfaces for producing
rendering-independent images.
|
| java.awt.print |
Provides classes and interfaces for a general printing API.
|
| java.beans |
Contains classes related to developing
beans -- components
based on the JavaBeans™ architecture.
|
| java.beans.beancontext |
Provides classes and interfaces relating to bean context.
|
| java.io |
Provides for system input and output through data streams,
serialization and the file system.
|
| java.lang |
Provides classes that are fundamental to the design of the Java
programming language.
|
| java.lang.annotation |
Provides library support for the Java programming language
annotation facility.
|
| java.lang.instrument |
Provides services that allow Java programming language agents to instrument programs running on the JVM.
|
| java.lang.invoke |
The
java.lang.invoke package contains dynamic language support provided directly by
the Java core class libraries and virtual machine. |
| java.lang.management |
Provides the management interfaces for monitoring and management of the
Java virtual machine and other components in the Java runtime.
|
| java.lang.ref |
Provides reference-object classes, which support a limited degree of
interaction with the garbage collector.
|
| java.lang.reflect |
Provides classes and interfaces for obtaining reflective
information about classes and objects.
|
| java.math |
Provides classes for performing arbitrary-precision integer
arithmetic (
BigInteger ) and arbitrary-precision decimal
arithmetic (BigDecimal ). |
| java.net |
Provides the classes for implementing networking applications.
|
| java.nio |
Defines buffers, which are containers for data, and provides an overview of the
other NIO packages.
|
| java.nio.channels |
Defines channels, which represent connections to entities that are capable of
performing I/O operations, such as files and sockets; defines selectors, for
multiplexed, non-blocking I/O operations.
|
| java.nio.channels.spi |
Service-provider classes for the
java.nio.channels package. |
| java.nio.charset |
Defines charsets, decoders, and encoders, for translating between bytes and
Unicode characters.
|
| java.nio.charset.spi |
Service-provider classes for the
java.nio.charset package. |
| java.nio.file |
Defines interfaces and classes for the Java virtual machine to access files,
file attributes, and file systems.
|
| java.nio.file.attribute |
Interfaces and classes providing access to file and file system attributes.
|
| java.nio.file.spi |
Service-provider classes for the
java.nio.file package. |
| java.rmi |
Provides the RMI package.
|
| java.rmi.activation |
Provides support for RMI Object Activation.
|
| java.rmi.dgc |
Provides classes and interface for RMI distributed
garbage-collection (DGC).
|
| java.rmi.registry |
Provides a class and two interfaces for the RMI registry.
|
| java.rmi.server |
Provides classes and interfaces for supporting the server
side of RMI.
|
| java.security |
Provides the classes and interfaces for the security framework.
|
| java.security.acl |
The classes and interfaces in this package have been
superseded by classes in the java.security package.
|
| java.security.cert |
Provides classes and interfaces for parsing and managing
certificates, certificate revocation lists (CRLs), and
certification paths.
|
| java.security.interfaces |
Provides interfaces for generating RSA (Rivest, Shamir and
Adleman AsymmetricCipher algorithm)
keys as defined in the RSA Laboratory Technical Note
PKCS#1, and DSA (Digital Signature
Algorithm) keys as defined in NIST's FIPS-186.
|
| java.security.spec |
Provides classes and interfaces for key specifications and algorithm
parameter specifications.
|
| java.sql |
Provides the API for accessing and processing data stored in a
data source (usually a relational database) using the
JavaTM programming language.
|
| java.text |
Provides classes and interfaces for handling text, dates, numbers, and messages
in a manner independent of natural languages.
|
| java.text.spi |
Service provider classes for the classes in the java.text package.
|
| java.util |
Contains the collections framework, legacy collection classes, event model,
date and time facilities, internationalization, and miscellaneous utility
classes (a string tokenizer, a random-number generator, and a bit array).
|
| java.util.concurrent |
Utility classes commonly useful in concurrent programming.
|
| java.util.concurrent.atomic |
A small toolkit of classes that support lock-free thread-safe
programming on single variables.
|
| java.util.concurrent.locks |
Interfaces and classes providing a framework for locking and waiting
for conditions that is distinct from built-in synchronization and
monitors.
|
| java.util.jar |
Provides classes for reading and writing the JAR (Java ARchive) file
format, which is based on the standard ZIP file format with an
optional manifest file.
|
| java.util.logging |
Provides the classes and interfaces of
the JavaTM 2
platform's core logging facilities.
|
| java.util.prefs |
This package allows applications to store and retrieve user and system
preference and configuration data.
|
| java.util.regex |
Classes for matching character sequences against patterns specified by regular
expressions.
|
| java.util.spi |
Service provider classes for the classes in the java.util package.
|
| java.util.zip |
Provides classes for reading and writing the standard ZIP and GZIP
file formats.
|
| javax.accessibility |
Defines a contract between user-interface components and an assistive technology
that provides access to those components.
|
| javax.activation | |
| javax.activity |
Contains Activity service related exceptions thrown by the ORB machinery during
unmarshalling.
|
| javax.annotation | |
| javax.annotation.processing |
Facilities for declaring annotation processors and for
allowing annotation processors to communicate with an annotation processing
tool environment.
|
| javax.crypto |
Provides the classes and interfaces for cryptographic operations.
|
| javax.crypto.interfaces |
Provides interfaces for Diffie-Hellman keys as defined in
RSA Laboratories' PKCS #3.
|
| javax.crypto.spec |
Provides classes and interfaces for key specifications and algorithm
parameter specifications.
|
| javax.imageio |
The main package of the Java Image I/O API.
|
| javax.imageio.event |
A package of the Java Image I/O API dealing with synchronous
notification of events during the reading and writing of images.
|
| javax.imageio.metadata |
A package of the Java Image I/O API dealing with reading and writing
metadata.
|
| javax.imageio.plugins.bmp |
Package containing the public classes used by the built-in BMP plug-in.
|
| javax.imageio.plugins.jpeg |
Classes supporting the built-in JPEG plug-in.
|
| javax.imageio.spi |
A package of the Java Image I/O API containing the plug-in interfaces
for readers, writers, transcoders, and streams, and a runtime
registry.
|
| javax.imageio.stream |
A package of the Java Image I/O API dealing with low-level I/O from
files and streams.
|
| javax.jws | |
| javax.jws.soap | |
| javax.lang.model |
Classes and hierarchies of packages used to model the Java
programming language.
|
| javax.lang.model.element |
Interfaces used to model elements of the Java programming language.
|
| javax.lang.model.type |
Interfaces used to model Java programming language types.
|
| javax.lang.model.util |
Utilities to assist in the processing of
program elements and
types.
|
| javax.management |
Provides the core classes for the Java Management Extensions.
|
| javax.management.loading |
Provides the classes which implement advanced dynamic
loading.
|
| javax.management.modelmbean |
Provides the definition of the ModelMBean classes.
|
| javax.management.monitor |
Provides the definition of the monitor classes.
|
| javax.management.openmbean |
Provides the open data types and Open MBean descriptor classes.
|
| javax.management.relation |
Provides the definition of the Relation Service.
|
| javax.management.remote |
Interfaces for remote access to
JMX MBean servers.
|
| javax.management.remote.rmi |
The RMI connector is a connector for the JMX Remote API that
uses RMI to transmit client requests to a remote MBean server.
|
| javax.management.timer |
Provides the definition of the Timer MBean.
|
| javax.naming |
Provides the classes and interfaces for accessing naming services.
|
| javax.naming.directory |
Extends the javax.naming package to provide functionality
for accessing directory services.
|
| javax.naming.event |
Provides support for event notification when accessing naming and
directory services.
|
| javax.naming.ldap |
Provides support for LDAPv3 extended operations and controls.
|
| javax.naming.spi | |
| javax.net |
Provides classes for networking applications.
|
| javax.net.ssl |
Provides classes for the secure socket package.
|
| javax.print |
Provides the principal classes and interfaces for the
JavaTM Print Service API.
|
| javax.print.attribute |
Provides classes and interfaces
that describe the types of JavaTM Print
Service attributes and how they can be collected into attribute sets.
|
| javax.print.attribute.standard |
Package javax.print.attribute.standard
contains classes for specific printing attributes.
|
| javax.print.event |
Package javax.print.event contains event classes and listener interfaces.
|
| javax.rmi |
Contains user APIs for RMI-IIOP.
|
| javax.rmi.CORBA |
Contains portability APIs for RMI-IIOP.
|
| javax.rmi.ssl |
Provides implementations of
RMIClientSocketFactory
and RMIServerSocketFactory over
the Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols. |
| javax.script |
The scripting API consists of interfaces and classes that define
Java TM Scripting Engines and provides
a framework for their use in Java applications.
|
| javax.security.auth |
This package provides a framework for authentication and
authorization.
|
| javax.security.auth.callback |
This package provides the classes necessary for services
to interact with applications in order to retrieve
information (authentication data including usernames
or passwords, for example) or to display information
(error and warning messages, for example).
|
| javax.security.auth.kerberos |
This package contains utility classes related to the Kerberos network
authentication protocol.
|
| javax.security.auth.login |
This package provides a pluggable authentication framework.
|
| javax.security.auth.spi |
This package provides the interface to be used for
implementing pluggable authentication modules.
|
| javax.security.auth.x500 |
This package contains the classes that should be used to store
X500 Principal and X500 Private Crendentials in a
Subject.
|
| javax.security.cert |
Provides classes for public key certificates.
|
| javax.security.sasl |
Contains class and interfaces for supporting SASL.
|
| javax.sound.midi |
Provides interfaces and classes for I/O, sequencing, and synthesis of MIDI
(Musical Instrument Digital Interface) data.
|
| javax.sound.midi.spi |
Supplies interfaces for service providers to implement when
offering new MIDI devices, MIDI file readers and writers, or sound bank readers.
|
| javax.sound.sampled |
Provides interfaces and classes for capture, processing, and playback of sampled audio data.
|
| javax.sound.sampled.spi |
Supplies abstract classes for service providers to subclass when
offering new audio devices, sound file readers and writers, or audio format converters.
|
| javax.sql |
Provides the API for server side data source access and processing from
the JavaTM programming language.
|
| javax.sql.rowset |
Standard interfaces and base classes for JDBC
RowSet
implementations. |
| javax.sql.rowset.serial |
Provides utility classes to allow serializable mappings between SQL types
and data types in the Java programming language.
|
| javax.sql.rowset.spi |
The standard classes and interfaces that a third party vendor has to
use in its implementation of a synchronization provider.
|
| javax.swing |
Provides a set of "lightweight"
(all-Java language) components that,
to the maximum degree possible, work the same on all platforms.
|
| javax.swing.border |
Provides classes and interface for drawing
specialized borders around a Swing component.
|
| javax.swing.colorchooser |
Contains classes and interfaces used by the
JColorChooser
component. |
| javax.swing.event |
Provides for events fired by Swing components.
|
| javax.swing.filechooser |
Contains classes and interfaces used by the
JFileChooser component. |
| javax.swing.plaf |
Provides one interface and many abstract classes that
Swing uses to provide its pluggable look-and-feel capabilities.
|
| javax.swing.plaf.basic |
Provides user interface objects built according to the
Basic look and feel.
|
| javax.swing.plaf.metal |
Provides user interface objects built according to
the Java look and feel (once codenamed Metal),
which is the default look and feel.
|
| javax.swing.plaf.multi |
Provides user interface objects that combine two or more look and feels.
|
| javax.swing.plaf.nimbus |
Provides user interface objects built according to the cross-platform
Nimbus look and feel.
|
| javax.swing.plaf.synth |
Synth is a skinnable look and feel in which all painting is
delegated.
|
| javax.swing.table |
Provides classes and interfaces for dealing with
javax.swing.JTable . |
| javax.swing.text |
Provides classes and interfaces that deal with editable
and noneditable text components.
|
| javax.swing.text.html |
Provides the class
HTMLEditorKit and supporting classes
for creating HTML text editors. |
| javax.swing.text.html.parser |
Provides the default HTML parser, along with support classes.
|
| javax.swing.text.rtf |
Provides a class (
RTFEditorKit ) for creating Rich-Text-Format
text editors. |
| javax.swing.tree |
Provides classes and interfaces for dealing with
javax.swing.JTree . |
| javax.swing.undo |
Allows developers to provide support for undo/redo
in applications such as text editors.
|
| javax.tools |
Provides interfaces for tools which can be invoked from a program,
for example, compilers.
|
| javax.transaction |
Contains three exceptions thrown by the ORB machinery during unmarshalling.
|
| javax.transaction.xa |
Provides the API that defines the contract between the transaction
manager and the resource manager, which allows the transaction
manager to enlist and delist resource objects (supplied by the
resource manager driver) in JTA transactions.
|
| javax.xml | |
| javax.xml.bind |
Provides a runtime binding framework for client applications including
unmarshalling, marshalling, and validation capabilities.
|
| javax.xml.bind.annotation |
Defines annotations for customizing Java program elements to XML Schema mapping.
|
| javax.xml.bind.annotation.adapters |
XmlAdapter and its spec-defined
sub-classes to allow arbitrary Java classes to be used with JAXB. |
| javax.xml.bind.attachment |
This package is implemented by a MIME-based package processor that
enables the interpretation and creation of optimized binary data
within an MIME-based package format.
|
| javax.xml.bind.helpers |
JAXB Provider Use Only: Provides partial default implementations for
some of the javax.xml.bind interfaces.
|
| javax.xml.bind.util |
Useful client utility classes.
|
| javax.xml.crypto |
Common classes for XML cryptography.
|
| javax.xml.crypto.dom |
DOM-specific classes for the
javax.xml.crypto package. |
| javax.xml.crypto.dsig |
Classes for generating and validating XML digital
signatures.
|
| javax.xml.crypto.dsig.dom |
DOM-specific classes for the
javax.xml.crypto.dsig package. |
| javax.xml.crypto.dsig.keyinfo |
Classes for parsing and processing
KeyInfo elements and structures. |
| javax.xml.crypto.dsig.spec |
Parameter classes for XML digital signatures.
|
| javax.xml.datatype |
XML/Java Type Mappings.
|
| javax.xml.namespace |
XML Namespace processing.
|
| javax.xml.parsers |
Provides classes allowing the processing of XML documents.
|
| javax.xml.soap |
Provides the API for creating and building SOAP messages.
|
| javax.xml.stream | |
| javax.xml.stream.events | |
| javax.xml.stream.util | |
| javax.xml.transform |
This package defines the generic APIs for processing transformation
instructions, and performing a transformation from source to result.
|
| javax.xml.transform.dom |
This package implements DOM-specific transformation APIs.
|
| javax.xml.transform.sax |
This package implements SAX2-specific transformation APIs.
|
| javax.xml.transform.stax |
Provides for StAX-specific transformation APIs.
|
| javax.xml.transform.stream |
This package implements stream- and URI- specific transformation APIs.
|
| javax.xml.validation |
This package provides an API for validation of XML documents.
|
| javax.xml.ws |
This package contains the core JAX-WS APIs.
|
| javax.xml.ws.handler |
This package defines APIs for message handlers.
|
| javax.xml.ws.handler.soap |
This package defines APIs for SOAP message handlers.
|
| javax.xml.ws.http |
This package defines APIs specific to the HTTP binding.
|
| javax.xml.ws.soap |
This package defines APIs specific to the SOAP binding.
|
| javax.xml.ws.spi |
This package defines SPIs for JAX-WS.
|
| javax.xml.ws.spi.http |
Provides HTTP SPI that is used for portable deployment of JAX-WS
web services in containers(for e.g.
|
| javax.xml.ws.wsaddressing |
This package defines APIs related to WS-Addressing.
|
| javax.xml.xpath |
This package provides an object-model neutral API for the
evaluation of XPath expressions and access to the evaluation
environment.
|
| org.ietf.jgss |
This package presents a framework that allows application developers to
make use of security services like authentication, data integrity and
data confidentiality from a variety of underlying security mechanisms
like Kerberos, using a unified API.
|
| org.omg.CORBA |
Provides the mapping of the OMG CORBA APIs to the JavaTM
programming language, including the class ORB, which is implemented
so that a programmer can use it as a fully-functional Object Request Broker
(ORB).
|
| org.omg.CORBA_2_3 |
The CORBA_2_3 package defines additions to existing CORBA interfaces
in the Java[tm] Standard Edition 6. These changes occurred in recent
revisions to the CORBA API defined by the OMG. The new methods were
added to interfaces derived from the corresponding interfaces in
the CORBA package. This provides backward compatibility and avoids
breaking the JCK tests.
|
| org.omg.CORBA_2_3.portable |
Provides methods for the input and output of value types, and contains
other updates to the
org/omg/CORBA/portable package. |
| org.omg.CORBA.DynAnyPackage |
Provides the exceptions used with the
DynAny interface
(InvalidValue ,
Invalid , InvalidSeq , and
TypeMismatch ). |
| org.omg.CORBA.ORBPackage |
Provides the exception
InvalidName , which is thrown
by the method ORB.resolve_initial_references
and the exception InconsistentTypeCode , which is thrown
by the Dynamic Any creation methods in the ORB class. |
| org.omg.CORBA.portable |
Provides a portability layer, that is, a set of ORB APIs
that makes it possible for code generated
by one vendor to run on another vendor's ORB.
|
| org.omg.CORBA.TypeCodePackage |
Provides the user-defined exceptions
BadKind
and Bounds , which are thrown by methods in
in the class TypeCode . |
| org.omg.CosNaming |
Provides a naming service for Java IDL.
|
| org.omg.CosNaming.NamingContextExtPackage |
This package contains the following classes, which are used in
org.omg.CosNaming.NamingContextExt:
|
| org.omg.CosNaming.NamingContextPackage |
This package contains Exception classes for the org.omg.CosNaming
package.
|
| org.omg.Dynamic |
This package contains the Dynamic module specified in the OMG Portable
Interceptor specification,
http://cgi.omg.org/cgi-bin/doc?ptc/2000-08-06, section 21.9.
|
| org.omg.DynamicAny |
Provides classes and interfaces that enable traversal of the data value
associated with an
any at
runtime, and extraction of the primitive constituents of the data value. |
| org.omg.DynamicAny.DynAnyFactoryPackage |
This package contains classes and exceptions from the DynAnyFactory
interface of the
DynamicAny module
specified in the OMG The Common Object Request Broker: Architecture and
Specification,
http://cgi.omg.org/cgi-bin/doc?formal/99-10-07, section 9.2.2.
|
| org.omg.DynamicAny.DynAnyPackage |
This package contains classes and exceptions from the DynAny
interface of the DynamicAny module
specified in the OMG The Common Object Request Broker: Architecture and
Specification,
http://cgi.omg.org/cgi-bin/doc?formal/99-10-07, section 9.2.
|
| org.omg.IOP |
This package contains the IOP module specified in the OMG document
The Common
Object Request Broker: Architecture and Specification,
http://cgi.omg.org/cgi-bin/doc?formal/99-10-07, section 13.6.
|
| org.omg.IOP.CodecFactoryPackage |
This package contains the exceptions
specified in the IOP::CodeFactory interface (as part of the Portable
Interceptors spec).
|
| org.omg.IOP.CodecPackage |
This package is generated from the IOP::Codec IDL interface
definition.
|
| org.omg.Messaging |
This package contains the Messaging module specified in the OMG CORBA
Messaging specification,
http://cgi.omg.org/cgi-bin/doc?formal/99-10-07.
|
| org.omg.PortableInterceptor |
Provides a mechanism to register ORB hooks through which ORB services
can intercept the normal flow of execution of the ORB.
|
| org.omg.PortableInterceptor.ORBInitInfoPackage |
This package contains the exceptions and typedefs from the ORBInitInfo
local interface of the PortableInterceptor module specified in the OMG
Portable Interceptor specification,
http://cgi.omg.org/cgi-bin/doc?ptc/2000-08-06, section 21.7.2.
|
| org.omg.PortableServer |
Provides classes and interfaces for making the server side of your applications
portable across multivendor ORBs.
|
| org.omg.PortableServer.CurrentPackage |
Provides method implementations with
access to the identity of the object on which the
method was invoked.
|
| org.omg.PortableServer.POAManagerPackage |
Encapsulates the processing
state of the POAs it is associated with.
|
| org.omg.PortableServer.POAPackage |
Allows programmers to construct object implementations that are portable
between different ORB products.
|
| org.omg.PortableServer.portable |
Provides classes and interfaces for making the server side of your applications
portable across multivendor ORBs.
|
| org.omg.PortableServer.ServantLocatorPackage |
Provides classes and interfaces for locating the servant.
|
| org.omg.SendingContext |
Provides support for the marshalling of value types.
|
| org.omg.stub.java.rmi |
Contains RMI-IIOP Stubs for the Remote types that occur in the
java.rmi package.
|
| org.w3c.dom |
Provides the interfaces for the Document Object Model (DOM) which is a
component API of the Java API for XML
Processing.
|
| org.w3c.dom.bootstrap | |
| org.w3c.dom.events | |
| org.w3c.dom.ls | |
| org.xml.sax |
This package provides the core SAX APIs.
|
| org.xml.sax.ext |
This package contains interfaces to SAX2 facilities that
conformant SAX drivers won't necessarily support.
|
| org.xml.sax.helpers |
This package contains "helper" classes, including
support for bootstrapping SAX-based applications.
|
Submit a bug or feature
For further API reference and developer documentation, see Java SE Documentation. That documentation contains more detailed, developer-targeted descriptions, with conceptual overviews, definitions of terms, workarounds, and working code examples.
Copyright © 1993, 2015, Oracle and/or its affiliates. All rights reserved.


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math::statistics - Basic statistical functions and procedures
The math::statistics package contains functions and procedures for basic statistical data analysis, such as:
Descriptive statistical parameters (mean, minimum, maximum, standard deviation)
Estimates of the distribution in the form of histograms and quantiles
Basic testing of hypotheses
Probability and cumulative density functions
It is meant to help in developing data analysis applications or doing ad hoc data analysis, it is not in itself a full application, nor is it intended to rival with full (non-)commercial statistical packages.
The purpose of this document is to describe the implemented procedures and provide some examples of their usage. As there is ample literature on the algorithms involved, we refer to relevant text books for more explanations. The package contains a fairly large number of public procedures. They can be distinguished in three sets: general procedures, procedures that deal with specific statistical distributions, list procedures to select or transform data and simple plotting procedures (these require Tk). Note: The data that need to be analyzed are always contained in a simple list. Missing values are represented as empty list elements. Note: With version 1.0.1 a mistake in the procs pdf-lognormal, cdf-lognormal and random-lognormal has been corrected. In previous versions the argument for the standard deviation was actually used as if it was the variance.
The general statistical procedures are:
Determine the mean value of the given list of data.
- List of data
Determine the minimum value of the given list of data.
- List of data
Determine the maximum value of the given list of data.
- List of data
Determine the number of non-missing data in the given list
- List of data
Determine the sample standard deviation of the data in the given list
- List of data
Determine the sample variance of the data in the given list
- List of data
Determine the population standard deviation of the data in the given list
- List of data
Determine the population variance of the data in the given list
- List of data
Determine the median of the data in the given list (Note that this requires sorting the data, which may be a costly operation)
- List of data
Determine a list of all the descriptive parameters: mean, minimum, maximum, number of data, sample standard deviation, sample variance, population standard deviation and population variance.
(This routine is called whenever either or all of the basic statistical parameters are required. Hence all calculations are done and the relevant values are returned.)
- List of data
Determine histogram information for the given list of data. Returns a list consisting of the number of values that fall into each interval. (The first interval consists of all values lower than the first limit, the last interval consists of all values greater than the last limit. There is one more interval than there are limits.)
Optionally, you can use weights to influence the histogram.
- List of upper limits (in ascending order) for the intervals of the histogram.
- List of data
- List of weights, one weight per value
Alternative implementation of the histogram procedure: the open end of the intervals is at the lower bound instead of the upper bound.
- List of upper limits (in ascending order) for the intervals of the histogram.
- List of data
- List of weights, one weight per value
Determine the correlation coefficient between two sets of data.
- First list of data
- Second list of data
Return the interval containing the mean value and one containing the standard deviation with a certain level of confidence (assuming a normal distribution)
- List of raw data values (small sample)
- Confidence level (0.95 or 0.99 for instance)
Test whether the mean value of a sample is in accordance with the estimated normal distribution with a certain probability. Returns 1 if the test succeeds or 0 if the mean is unlikely to fit the given distribution.
- List of raw data values (small sample)
- Estimated mean of the distribution
- Estimated stdev of the distribution
- Probability level (0.95 or 0.99 for instance)
Test whether the given data follow a normal distribution with a certain level of significance. Returns 1 if the data are normally distributed within the level of significance, returns 0 if not. The underlying test is the Lilliefors test. Smaller values of the significance mean a stricter testing.
- List of raw data values
- Significance level (one of 0.01, 0.05, 0.10, 0.15 or 0.20). For compatibility reasons the values "1-significance", 0.80, 0.85, 0.90, 0.95 or 0.99 are also accepted.
Compatibility issue: the original implementation and documentation used the term "confidence" and used a value 1-significance (see ticket 2812473fff). This has been corrected as of version 0.9.3.
Returns the goodness of fit to a normal distribution according to Lilliefors. The higher the number, the more likely the data are indeed normally distributed. The test requires at least five data points.
- List of raw data values
Determine if two data sets have the same median according to the Tukey-Duckworth test. The procedure returns 0 if the medians are unequal, 1 if they are equal, -1 if the test can not be conducted (the smallest value must be in a different set than the greatest value). # # Arguments: # list1 Values in the first data set # list2 Values in the second data set # significance Significance level (either 0.05, 0.01 or 0.001) # # Returns: Test whether the given data follow a normal distribution with a certain level of significance. Returns 1 if the data are normally distributed within the level of significance, returns 0 if not. The underlying test is the Lilliefors test. Smaller values of the significance mean a stricter testing.
- First list of data
- Second list of data
- Significance level (either 0.05, 0.01 or 0.001)
Determine if two or more groups with normally distributed data have the same means. The procedure returns 0 if the means are likely unequal, 1 if they are. This is a one-way ANOVA test. The groups may also be stored in a nested list: The procedure returns a list of the comparison results for each pair of groups. Each element of this list contains: the index of the first group and that of the second group, whether the means are likely to be different (1) or not (0) and the confidence interval the conclusion is based on. The groups may also be stored in a nested list:
test-anova-F 0.05 $A $B $C # # Or equivalently: # test-anova-F 0.05 [list $A $B $C]
- Significance level
- Two or more groups of data to be checked
Determine if two or more groups with normally distributed data have the same means, using Tukey's range test. It is complementary to the ANOVA test. The procedure returns a list of the comparison results for each pair of groups. Each element of this list contains: the index of the first group and that of the second group, whether the means are likely to be different (1) or not (0) and the confidence interval the conclusion is based on. The groups may also be stored in a nested list, just as with the ANOVA test.
- Significance level - either 0.05 or 0.01
- Two or more groups of data to be checked
Determine if one or more groups with normally distributed data have the same means as the group of control data, using Dunnett's test. It is complementary to the ANOVA test. The procedure returns a list of the comparison results for each group with the control group. Each element of this list contains: whether the means are likely to be different (1) or not (0) and the confidence interval the conclusion is based on. The groups may also be stored in a nested list, just as with the ANOVA test.
Note: some care is required if there is only one group to compare the control with:
test-Dunnett-F 0.05 $control [list $A]
Otherwise the group A is split up into groups of one element - this is due to an ambiguity.
- Significance level - either 0.05 or 0.01
- One or more groups of data to be checked
Return the quantiles for a given set of data
- List of raw data values
- Confidence level (0.95 or 0.99 for instance) or a list of confidence levels.
Return the quantiles based on histogram information (alternative to the call with two arguments)
- List of upper limits from histogram
- List of counts for for each interval in histogram
- Confidence level (0.95 or 0.99 for instance) or a list of confidence levels.
Return the autocorrelation function as a list of values (assuming equidistance between samples, about 1/2 of the number of raw data)
The correlation is determined in such a way that the first value is always 1 and all others are equal to or smaller than 1. The number of values involved will diminish as the "time" (the index in the list of returned values) increases
- Raw data for which the autocorrelation must be determined
Return the cross-correlation function as a list of values (assuming equidistance between samples, about 1/2 of the number of raw data)
The correlation is determined in such a way that the values can never exceed 1 in magnitude. The number of values involved will diminish as the "time" (the index in the list of returned values) increases.
- First list of data
- Second list of data
Determine reasonable limits based on mean and standard deviation for a histogram Convenience function - the result is suitable for the histogram function.
- Mean of the data
- Standard deviation
- Number of limits to generate (defaults to 8)
Determine reasonable limits based on a minimum and maximum for a histogram
Convenience function - the result is suitable for the histogram function.
- Expected minimum
- Expected maximum
- Number of limits to generate (defaults to 8)
Determine the coefficients for a linear regression between two series of data (the model: Y = A + B*X). Returns a list of parameters describing the fit
- List of independent data
- List of dependent data to be fitted
- (Optional) compute the intercept (1, default) or fit to a line through the origin (0)
The result consists of the following list:
(Estimate of) Intercept A
(Estimate of) Slope B
Standard deviation of Y relative to fit
Correlation coefficient R2
Number of degrees of freedom df
Standard error of the intercept A
Significance level of A
Standard error of the slope B
Significance level of B
Determine the difference between actual data and predicted from the linear model.
Returns a list of the differences between the actual data and the predicted values.
- List of independent data
- List of dependent data to be fitted
- (Optional) compute the intercept (1, default) or fit to a line through the origin (0)
Determine if two set of samples, each from a binomial distribution, differ significantly or not (implying a different parameter).
Returns the "chi-square" value, which can be used to the determine the significance.
- Number of outcomes with the first value from the first sample.
- Number of outcomes with the first value from the second sample.
- Number of outcomes with the second value from the first sample.
- Number of outcomes with the second value from the second sample.
Determine if two set of samples, each from a binomial distribution, differ significantly or not (implying a different parameter).
Returns a short report, useful in an interactive session.
- Number of outcomes with the first value from the first sample.
- Number of outcomes with the first value from the second sample.
- Number of outcomes with the second value from the first sample.
- Number of outcomes with the second value from the second sample.
Determine the control limits for an xbar chart. The number of data in each subsample defaults to 4. At least 20 subsamples are required.
Returns the mean, the lower limit, the upper limit and the number of data per subsample.
- List of observed data
- Number of data per subsample
Determine the control limits for an R chart. The number of data in each subsample (nsamples) defaults to 4. At least 20 subsamples are required.
Returns the mean range, the lower limit, the upper limit and the number of data per subsample.
- List of observed data
- Number of data per subsample
Determine if the data exceed the control limits for the xbar chart.
Returns a list of subsamples (their indices) that indeed violate the limits.
- Control limits as returned by the "control-xbar" procedure
- List of observed data
Determine if the data exceed the control limits for the R chart.
Returns a list of subsamples (their indices) that indeed violate the limits.
- Control limits as returned by the "control-Rchart" procedure
- List of observed data
Check if the population medians of two or more groups are equal with a given confidence level, using the Kruskal-Wallis test.
- Confidence level to be used (0-1)
- Two or more lists of data
Compute the statistical parameters for the Kruskal-Wallis test. Returns the Kruskal-Wallis statistic and the probability that that value would occur assuming the medians of the populations are equal.
- Two or more lists of data
Compute the Levene statistic to determine if groups of data have the same variance (are homoscadastic) or not. The data are organised in groups. This version uses the mean of the data as the measure to determine the deviations. The statistic is equivalent to an F statistic with degrees of freedom k-1 and N-k, k being the number of groups and N the total number of data.
- List of groups of data
Compute the Brown-Forsythe statistic to determine if groups of data have the same variance (are homoscadastic) or not. Like the Levene test, but this version uses the median of the data.
- List of groups of data
Rank the groups of data with respect to the complete set. Returns a list consisting of the group ID, the value and the rank (possibly a rational number, in case of ties) for each data item.
- Two or more lists of data
Compute the Wilcoxon test statistic to determine if two samples have the same median or not. (The statistic can be regarded as standard normal, if the sample sizes are both larger than 10.) Returns the value of this statistic.
- List of data comprising the first sample
- List of data comprising the second sample
Return the Spearman rank correlation as an alternative to the ordinary (Pearson's) correlation coefficient. The two samples should have the same number of data.
- First list of data
- Second list of data
Return the Spearman rank correlation as an alternative to the ordinary (Pearson's) correlation coefficient as well as additional data. The two samples should have the same number of data. The procedure returns the correlation coefficient, the number of data pairs used and the z-score, an approximately standard normal statistic, indicating the significance of the correlation.
- First list of data
- Second list of data
Return the density function based on kernel density estimation. The procedure is controlled by a small set of options, each of which is given a reasonable default.
The return value consists of three lists: the centres of the bins, the associated probability density and a list of computational parameters (begin and end of the interval, mean and standard deviation and the used bandwidth). The computational parameters can be used for further analysis.
- The data to be examined
- Option-value pairs:
Per data point the weight (default: 1 for all data)
Bandwidth to be used for the estimation (default: determined from standard deviation)
Number of bins to be returned (default: 100)
Begin and end of the interval for which the density is returned (default: mean +/- 3*standard deviation)
Kernel to be used (One of: gaussian, cosine, epanechnikov, uniform, triangular, biweight, logistic; default: gaussian)
Create a subsample or subsamples from a given list of data. The data in the samples are chosen from this list - multiples may occur. If there is only one subsample, the sample itself is returned (as a list of "sampleSize" values), otherwise a list of samples is returned.
List of values to chose from
Number of values per sample
Number of samples (default: 1)
Compute the Wasserstein distance or earth mover's distance for two equidstantly spaced histograms or probability densities. The histograms need not to be normalised to sum to one, but they must have the same number of entries.
Note: the histograms are assumed to be based on the same equidistant intervals. As the bounds are not passed, the value is expressed in the length of the intervals.
List of values for the first histogram/probability density
List of values for the second histogram/probability density
Compute the Kullback-Leibler (KL) divergence for two equidstantly spaced histograms or probability densities. The histograms need not to be normalised to sum to one, but they must have the same number of entries.
Note: the histograms are assumed to be based on the same equidistant intervals. As the bounds are not passed, the value is expressed in the length of the intervals.
Note also that the KL divergence is not symmetric and that the second histogram should not contain zeroes in places where the first histogram has non-zero values.
List of values for the first histogram/probability density
List of values for the second histogram/probability density
Estimate the coefficients of the logistic model that fits the data best. The data consist of independent x-values and the outcome 0 or 1 for each of the x-values. The result can be used to estimate the probability that a certain x-value gives 1.
List of values for which the success (1) or failure (0) is known
List of successes or failures corresponding to each value in xdata.
Calculate the probability of success for the value x given the coefficients of the logistic model.
List of coefficients as determine by the logistic-model command
X-value for which the probability needs to be determined
Besides the linear regression with a single independent variable, the statistics package provides two procedures for doing ordinary least squares (OLS) and weighted least squares (WLS) linear regression with several variables. They were written by Eric Kemp-Benedict.
In addition to these two, it provides a procedure (tstat) for calculating the value of the t-statistic for the specified number of degrees of freedom that is required to demonstrate a given level of significance.
Note: These procedures depend on the math::linearalgebra package.
Description of the procedures
Returns the value of the t-distribution t* satisfying
P(t*) = 1 - alpha/2 P(-t*) = alpha/2
for the number of degrees of freedom dof.
Given a sample of normally-distributed data x, with an estimate xbar for the mean and sbar for the standard deviation, the alpha confidence interval for the estimate of the mean can be calculated as
( xbar - t* sbar , xbar + t* sbar)
The return values from this procedure can be compared to an estimated t-statistic to determine whether the estimated value of a parameter is significantly different from zero at the given confidence level.
Number of degrees of freedom
Confidence level of the t-distribution. Defaults to 0.05.
Carries out a weighted least squares linear regression for the data points provided, with weights assigned to each point.
The linear model is of the form
y = b0 + b1 * x1 + b2 * x2 ... + bN * xN + error
and each point satisfies
yi = b0 + b1 * xi1 + b2 * xi2 + ... + bN * xiN + Residual_i
The procedure returns a list with the following elements:
The r-squared statistic
The adjusted r-squared statistic
A list containing the estimated coefficients b1, ... bN, b0 (The constant b0 comes last in the list.)
A list containing the standard errors of the coefficients
A list containing the 95% confidence bounds of the coefficients, with each set of bounds returned as a list with two values
Arguments:
A list consisting of: the weight for the first observation, the data for the first observation (as a sublist), the weight for the second observation (as a sublist) and so on. The sublists of data are organised as lists of the value of the dependent variable y and the independent variables x1, x2 to xN.
Carries out an ordinary least squares linear regression for the data points provided.
This procedure simply calls ::mvlinreg::wls with the weights set to 1.0, and returns the same information.
Example of the use:
# Store the value of the unicode value for the "+/-" character set pm "\u00B1" # Provide some data set data {{ -.67 14.18 60.03 -7.5 } { 36.97 15.52 34.24 14.61 } {-29.57 21.85 83.36 -7. } {-16.9 11.79 51.67 -6.56 } { 14.09 16.24 36.97 -12.84} { 31.52 20.93 45.99 -25.4 } { 24.05 20.69 50.27 17.27} { 22.23 16.91 45.07 -4.3 } { 40.79 20.49 38.92 -.73 } {-10.35 17.24 58.77 18.78}} # Call the ols routine set results [::math::statistics::mv-ols $data] # Pretty-print the results puts "R-squared: [lindex $results 0]" puts "Adj R-squared: [lindex $results 1]" puts "Coefficients $pm s.e. -- \[95% confidence interval\]:" foreach val [lindex $results 2] se [lindex $results 3] bounds [lindex $results 4] { set lb [lindex $bounds 0] set ub [lindex $bounds 1] puts " $val $pm $se -- \[$lb to $ub\]" }
In the literature a large number of probability distributions can be found. The statistics package supports:
The normal or Gaussian distribution as well as the log-normal distribution
The uniform distribution - equal probability for all data within a given interval
The exponential distribution - useful as a model for certain extreme-value distributions.
The gamma distribution - based on the incomplete Gamma integral
The beta distribution
The chi-square distribution
The student's T distribution
The Poisson distribution
The Pareto distribution
The Gumbel distribution
The Weibull distribution
The Cauchy distribution
The F distribution (only the cumulative density function)
PM - binomial.
In principle for each distribution one has procedures for:
The probability density (pdf-*)
The cumulative density (cdf-*)
Quantiles for the given distribution (quantiles-*)
Histograms for the given distribution (histogram-*)
List of random values with the given distribution (random-*)
The following procedures have been implemented:
Return the probability of a given value for a normal distribution with given mean and standard deviation.
- Mean value of the distribution
- Standard deviation of the distribution
- Value for which the probability is required
Return the probability of a given value for a log-normal distribution with given mean and standard deviation.
- Mean value of the distribution
- Standard deviation of the distribution
- Value for which the probability is required
Return the probability of a given value for an exponential distribution with given mean.
- Mean value of the distribution
- Value for which the probability is required
Return the probability of a given value for a uniform distribution with given extremes.
- Minimum value of the distribution
- Maximum value of the distribution
- Value for which the probability is required
Return the probability of a given value for a triangular distribution with given extremes. If the argument min is lower than the argument max, then smaller values have higher probability and vice versa. In the first case the probability density function is of the form f(x) = 2(1-x) and the other case it is of the form f(x) = 2x.
- Minimum value of the distribution
- Maximum value of the distribution
- Value for which the probability is required
Return the probability of a given value for a symmetric triangular distribution with given extremes.
- Minimum value of the distribution
- Maximum value of the distribution
- Value for which the probability is required
Return the probability of a given value for a Gamma distribution with given shape and rate parameters
- Shape parameter
- Rate parameter
- Value for which the probability is required
Return the probability of a given number of occurrences in the same interval (k) for a Poisson distribution with given mean (mu)
- Mean number of occurrences
- Number of occurences
Return the probability of a given value for a chi square distribution with given degrees of freedom
- Degrees of freedom
- Value for which the probability is required
Return the probability of a given value for a Student's t distribution with given degrees of freedom
- Degrees of freedom
- Value for which the probability is required
Return the probability of a given value for a Gamma distribution with given shape and rate parameters
- Shape parameter
- Rate parameter
- Value for which the probability is required
Return the probability of a given value for a Beta distribution with given shape parameters
- First shape parameter
- Second shape parameter
- Value for which the probability is required
Return the probability of a given value for a Weibull distribution with given scale and shape parameters
- Scale parameter
- Shape parameter
- Value for which the probability is required
Return the probability of a given value for a Gumbel distribution with given location and shape parameters
- Location parameter
- Shape parameter
- Value for which the probability is required
Return the probability of a given value for a Pareto distribution with given scale and shape parameters
- Scale parameter
- Shape parameter
- Value for which the probability is required
Return the probability of a given value for a Cauchy distribution with given location and shape parameters. Note that the Cauchy distribution has no finite higher-order moments.
- Location parameter
- Shape parameter
- Value for which the probability is required
Return the probability of a given value for a Laplace distribution with given location and shape parameters. The Laplace distribution consists of two exponential functions, is peaked and has heavier tails than the normal distribution.
- Location parameter (mean)
- Shape parameter
- Value for which the probability is required
Return the probability of a given value for a Kumaraswamy distribution with given parameters a and b. The Kumaraswamy distribution is related to the Beta distribution, but has a tractable cumulative distribution function.
- Parameter a
- Parameter b
- Value for which the probability is required
Return the probability of a given value for a negative binomial distribution with an allowed number of failures and the probability of success.
- Allowed number of failures (at least 1)
- Probability of success
- Number of successes for which the probability is to be returned
Return the cumulative probability of a given value for a normal distribution with given mean and standard deviation, that is the probability for values up to the given one.
- Mean value of the distribution
- Standard deviation of the distribution
- Value for which the probability is required
Return the cumulative probability of a given value for a log-normal distribution with given mean and standard deviation, that is the probability for values up to the given one.
- Mean value of the distribution
- Standard deviation of the distribution
- Value for which the probability is required
Return the cumulative probability of a given value for an exponential distribution with given mean.
- Mean value of the distribution
- Value for which the probability is required
Return the cumulative probability of a given value for a uniform distribution with given extremes.
- Minimum value of the distribution
- Maximum value of the distribution
- Value for which the probability is required
Return the cumulative probability of a given value for a triangular distribution with given extremes. If xmin < xmax, then lower values have a higher probability and vice versa, see also pdf-triangular
- Minimum value of the distribution
- Maximum value of the distribution
- Value for which the probability is required
Return the cumulative probability of a given value for a symmetric triangular distribution with given extremes.
- Minimum value of the distribution
- Maximum value of the distribution
- Value for which the probability is required
Return the cumulative probability of a given value for a Student's t distribution with given number of degrees.
- Number of degrees of freedom
- Value for which the probability is required
Return the cumulative probability of a given value for a Gamma distribution with given shape and rate parameters.
- Shape parameter
- Rate parameter
- Value for which the cumulative probability is required
Return the cumulative probability of a given number of occurrences in the same interval (k) for a Poisson distribution with given mean (mu).
- Mean number of occurrences
- Number of occurences
Return the cumulative probability of a given value for a Beta distribution with given shape parameters
- First shape parameter
- Second shape parameter
- Value for which the probability is required
Return the cumulative probability of a given value for a Weibull distribution with given scale and shape parameters.
- Scale parameter
- Shape parameter
- Value for which the probability is required
Return the cumulative probability of a given value for a Gumbel distribution with given location and scale parameters.
- Location parameter
- Scale parameter
- Value for which the probability is required
Return the cumulative probability of a given value for a Pareto distribution with given scale and shape parameters
- Scale parameter
- Shape parameter
- Value for which the probability is required
Return the cumulative probability of a given value for a Cauchy distribution with given location and scale parameters.
- Location parameter
- Scale parameter
- Value for which the probability is required
Return the cumulative probability of a given value for an F distribution with nf1 and nf2 degrees of freedom.
- Degrees of freedom for the numerator
- Degrees of freedom for the denominator
- Value for which the probability is required
Return the cumulative probability of a given value for a Laplace distribution with given location and shape parameters. The Laplace distribution consists of two exponential functions, is peaked and has heavier tails than the normal distribution.
- Location parameter (mean)
- Shape parameter
- Value for which the probability is required
Return the cumulative probability of a given value for a Kumaraswamy distribution with given parameters a and b. The Kumaraswamy distribution is related to the Beta distribution, but has a tractable cumulative distribution function.
- Parameter a
- Parameter b
- Value for which the probability is required
Return the cumulative probability of a given value for a negative binomial distribution with an allowed number of failures and the probability of success.
- Allowed number of failures (at least 1)
- Probability of success
- Greatest number of successes
Return a list of values and their empirical probability. The values are sorted in increasing order. (The implementation follows the description at the corresponding Wikipedia page)
- List of data to be examined
Return a list of "number" random values satisfying a normal distribution with given mean and standard deviation.
- Mean value of the distribution
- Standard deviation of the distribution
- Number of values to be returned
Return a list of "number" random values satisfying a log-normal distribution with given mean and standard deviation.
- Mean value of the distribution
- Standard deviation of the distribution
- Number of values to be returned
Return a list of "number" random values satisfying an exponential distribution with given mean.
- Mean value of the distribution
- Number of values to be returned
Return a list of "number" random values satisfying a uniform distribution with given extremes.
- Minimum value of the distribution
- Maximum value of the distribution
- Number of values to be returned
Return a list of "number" random values satisfying a triangular distribution with given extremes. If xmin < xmax, then lower values have a higher probability and vice versa (see also pdf-triangular.
- Minimum value of the distribution
- Maximum value of the distribution
- Number of values to be returned
Return a list of "number" random values satisfying a symmetric triangular distribution with given extremes.
- Minimum value of the distribution
- Maximum value of the distribution
- Number of values to be returned
Return a list of "number" random values satisfying a Gamma distribution with given shape and rate parameters.
- Shape parameter
- Rate parameter
- Number of values to be returned
Return a list of "number" random values satisfying a Poisson distribution with given mean.
- Mean of the distribution
- Number of values to be returned
Return a list of "number" random values satisfying a chi square distribution with given degrees of freedom.
- Degrees of freedom
- Number of values to be returned
Return a list of "number" random values satisfying a Student's t distribution with given degrees of freedom.
- Degrees of freedom
- Number of values to be returned
Return a list of "number" random values satisfying a Beta distribution with given shape parameters.
- First shape parameter
- Second shape parameter
- Number of values to be returned
Return a list of "number" random values satisfying a Weibull distribution with given scale and shape parameters.
- Scale parameter
- Shape parameter
- Number of values to be returned
Return a list of "number" random values satisfying a Gumbel distribution with given location and scale parameters.
- Location parameter
- Scale parameter
- Number of values to be returned
Return a list of "number" random values satisfying a Pareto distribution with given scale and shape parameters.
- Scale parameter
- Shape parameter
- Number of values to be returned
Return a list of "number" random values satisfying a Cauchy distribution with given location and scale parameters.
- Location parameter
- Scale parameter
- Number of values to be returned
Return a list of "number" random values satisfying a Laplace distribution with given location and shape parameters. The Laplace distribution consists of two exponential functions, is peaked and has heavier tails than the normal distribution.
- Location parameter (mean)
- Shape parameter
- Number of values to be returned
Return a list of "number" random values satisying a Kumaraswamy distribution with given parameters a and b. The Kumaraswamy distribution is related to the Beta distribution, but has a tractable cumulative distribution function.
- Parameter a
- Parameter b
- Number of values to be returned
Return a list of "number" random values satisying a negative binomial distribution.
- Allowed number of failures (at least 1)
- Probability of success
- Number of values to be returned
Return the expected histogram for a uniform distribution.
- Minimum value of the distribution
- Maximum value of the distribution
- Upper limits for the buckets in the histogram
- Total number of "observations" in the histogram
Evaluate the incomplete Gamma integral
1 / x p-1 P(p,x) = -------- | dt exp(-t) * t Gamma(p) / 0
- Value of x (limit of the integral)
- Value of p in the integrand
- Required tolerance (default: 1.0e-9)
Evaluate the incomplete Beta integral
- First shape parameter
- Second shape parameter
- Value of x (limit of the integral)
- Required tolerance (default: 1.0e-9)
Estimate the parameters for the Pareto distribution that comes closest to the given values. Returns the estimated scale and shape parameters, as well as the standard error for the shape parameter.
- List of values, assumed to be distributed according to a Pareto distribution
Estimate the parameter for the exponential distribution that comes closest to the given values. Returns an estimate of the one parameter and of the standard error.
- List of values, assumed to be distributed according to an exponential distribution
Estimate the parameters for the Laplace distribution that comes closest to the given values. Returns an estimate of respectively the location and scale parameters, based on maximum likelihood.
- List of values, assumed to be distributed according to an exponential distribution
Estimate the probability of success for the negative binomial distribution that comes closest to the given values. The allowed number of failures must be given.
- Allowed number of failures (at least 1)
- List of values, assumed to be distributed according to a negative binomial distribution.
TO DO: more function descriptions to be added
The data manipulation procedures act on lists or lists of lists:
Return a list consisting of the data for which the logical expression is true (this command works analogously to the command foreach).
- Name of the variable used in the expression
- List of data
- Logical expression using the variable name
Return a list consisting of the data that are transformed via the expression.
- Name of the variable used in the expression
- List of data
- Expression to be used to transform (map) the data
Return a list consisting of the counts of all data in the sublists of the "list" argument for which the expression is true.
- Name of the variable used in the expression
- List of sublists, each containing the data
- Logical expression to test the data (defaults to "true").
Routine PM - not implemented yet
The following simple plotting procedures are available:
Set the scale for a plot in the given canvas. All plot routines expect this function to be called first. There is no automatic scaling provided.
- Canvas widget to use
- Minimum x value
- Maximum x value
- Minimum y value
- Maximum y value
Create a simple XY plot in the given canvas - the data are shown as a collection of dots. The tag can be used to manipulate the appearance.
- Canvas widget to use
- Series of independent data
- Series of dependent data
- Tag to give to the plotted data (defaults to xyplot)
Create a simple XY plot in the given canvas - the data are shown as a line through the data points. The tag can be used to manipulate the appearance.
- Canvas widget to use
- Series of independent data
- Series of dependent data
- Tag to give to the plotted data (defaults to xyplot)
Create a simple XY plot in the given canvas - the data are shown as a collection of dots. The horizontal coordinate is equal to the index. The tag can be used to manipulate the appearance. This type of presentation is suitable for autocorrelation functions for instance or for inspecting the time-dependent behaviour.
- Canvas widget to use
- Series of dependent data
- Tag to give to the plotted data (defaults to xyplot)
Create a simple XY plot in the given canvas - the data are shown as a line. See plot-tdata for an explanation.
- Canvas widget to use
- Series of dependent data
- Tag to give to the plotted data (defaults to xyplot)
Create a simple histogram in the given canvas
- Canvas widget to use
- Series of bucket counts
- Series of upper limits for the buckets
- Tag to give to the plotted data (defaults to xyplot)
The following procedures are yet to be implemented:
F-test-stdev
interval-mean-stdev
histogram-normal
histogram-exponential
test-histogram
test-corr
quantiles-*
fourier-coeffs
fourier-residuals
onepar-function-fit
onepar-function-residuals
plot-linear-model
subdivide
The code below is a small example of how you can examine a set of data:
# Simple example: # - Generate data (as a cheap way of getting some) # - Perform statistical analysis to describe the data # package require math::statistics # # Two auxiliary procs # proc pause {time} { set wait 0 after [expr {$time*1000}] {set ::wait 1} vwait wait } proc print-histogram {counts limits} { foreach count $counts limit $limits { if { $limit != {} } { puts [format "<%12.4g\t%d" $limit $count] set prev_limit $limit } else { puts [format ">%12.4g\t%d" $prev_limit $count] } } } # # Our source of arbitrary data # proc generateData { data1 data2 } { upvar 1 $data1 _data1 upvar 1 $data2 _data2 set d1 0.0 set d2 0.0 for { set i 0 } { $i < 100 } { incr i } { set d1 [expr {10.0-2.0*cos(2.0*3.1415926*$i/24.0)+3.5*rand()}] set d2 [expr {0.7*$d2+0.3*$d1+0.7*rand()}] lappend _data1 $d1 lappend _data2 $d2 } return {} } # # The analysis session # package require Tk console show canvas .plot1 canvas .plot2 pack .plot1 .plot2 -fill both -side top generateData data1 data2 puts "Basic statistics:" set b1 [::math::statistics::basic-stats $data1] set b2 [::math::statistics::basic-stats $data2] foreach label {mean min max number stdev var} v1 $b1 v2 $b2 { puts "$label\t$v1\t$v2" } puts "Plot the data as function of \"time\" and against each other" ::math::statistics::plot-scale .plot1 0 100 0 20 ::math::statistics::plot-scale .plot2 0 20 0 20 ::math::statistics::plot-tline .plot1 $data1 ::math::statistics::plot-tline .plot1 $data2 ::math::statistics::plot-xydata .plot2 $data1 $data2 puts "Correlation coefficient:" puts [::math::statistics::corr $data1 $data2] pause 2 puts "Plot histograms" .plot2 delete all ::math::statistics::plot-scale .plot2 0 20 0 100 set limits [::math::statistics::minmax-histogram-limits 7 16] set histogram_data [::math::statistics::histogram $limits $data1] ::math::statistics::plot-histogram .plot2 $histogram_data $limits puts "First series:" print-histogram $histogram_data $limits pause 2 set limits [::math::statistics::minmax-histogram-limits 0 15 10] set histogram_data [::math::statistics::histogram $limits $data2] ::math::statistics::plot-histogram .plot2 $histogram_data $limits d2 .plot2 itemconfigure d2 -fill red puts "Second series:" print-histogram $histogram_data $limits puts "Autocorrelation function:" set autoc [::math::statistics::autocorr $data1] puts [::math::statistics::map $autoc {[format "%.2f" $x]}] puts "Cross-correlation function:" set crossc [::math::statistics::crosscorr $data1 $data2] puts [::math::statistics::map $crossc {[format "%.2f" $x]}] ::math::statistics::plot-scale .plot1 0 100 -1 4 ::math::statistics::plot-tline .plot1 $autoc "autoc" ::math::statistics::plot-tline .plot1 $crossc "crossc" .plot1 itemconfigure autoc -fill green .plot1 itemconfigure crossc -fill yellow puts "Quantiles: 0.1, 0.2, 0.5, 0.8, 0.9" puts "First: [::math::statistics::quantiles $data1 {0.1 0.2 0.5 0.8 0.9}]" puts "Second: [::math::statistics::quantiles $data2 {0.1 0.2 0.5 0.8 0.9}]"
If you run this example, then the following should be clear:
There is a strong correlation between two time series, as displayed by the raw data and especially by the correlation functions.
Both time series show a significant periodic component
The histograms are not very useful in identifying the nature of the time series - they do not show the periodic nature.
This document, and the package it describes, will undoubtedly contain bugs and other problems. Please report such in the category math :: statistics of the Tcllib Trackers. Please also report any ideas for enhancements you may have for either package and/or documentation.
When proposing code changes, please provide unified diffs, i.e the output of diff -u.
Note further that attachments are strongly preferred over inlined patches. Attachments can be made by going to the Edit form of the ticket immediately after its creation, and then using the left-most button in the secondary navigation bar.
Mathematics


Run the following from the command line:
sudo gem install beats
This will download BEATS from rubygems.org and add a beats
command to your path.
The current version of BEATS is 1.2.0. If you have an older version, you can update with this:
sudo gem update beats
Not sure what version you have? Run beats -v
.
BEATS is not terribly useful unless you have drum sounds to use with it. To get you started, here are 20 high-quality drum sounds sampled from a Casio SA-20 keyboard that the author got when he was 8 years old: casio_sa20_drum_sounds.zip
It’s hosted at GitHub.
Copyright © 2010 Joel Strait


Some title
Paragaph 1.1
Some subtitle
Paragraph 2.2
Paragraph 2.3
Some subtitle
Paragraph 2.4
Paragraph 2.5
Paragaph 1.1
Paragraph 2.2
Paragraph 2.3
Paragraph 2.4
Paragraph 2.5
Paragaph 1.6
Paragraph 2.7
Paragraph 2.8
Paragraph 2.9
Paragraph 2.10
Paragaph 1.11
Paragraph 2.12
Paragraph 2.13
Paragraph 2.14
Paragraph 2.15


Classes
The following classes are available globally.
-
Undocumented
See moreDeclaration
Swift
@UIApplicationMain class AppDelegate : UIResponder, UIApplicationDelegate
-
The purpose of the
AuthenticateVC
view controller is to allow user to navigate to Instagram page for Authentication and also some visual effects related to app.There’s a matching scene in the Authenticate.storyboard file. Go to Interface Builder for details.
The
See moreAuthenticateVC
class is a subclass of theUIViewController
, and it conforms to theUIScrollViewDelegate
protocol.Declaration
Swift
class AuthenticateVC : UIViewController
-
The purpose of the
WebViewVC
view controller is to allow user to authenticate via Instagram and provide access of their personal details.There’s a matching scene in the Authenticate.storyboard file. Go to Interface Builder for details.
The
See moreWebViewVC
class is a subclass of theUIViewController
, and it conforms to theWKNavigationDelegate
protocol.Declaration
Swift
class WebViewVC : UIViewController
-
The purpose of the
ChooseLanguageVC
view controller is to allow user to choose different language.There’s a matching scene in the Home.storyboard file. Go to Interface Builder for details.
The
See moreChooseLanguageVC
class is a subclass of theUIViewController
, and it conforms to theUITableViewDelegate
,UITableViewDataSource
protocol.Declaration
Swift
class ChooseLanguageVC : UIViewController, UITableViewDelegate, UITableViewDataSource
-
The purpose of the
HomeVC
view controller is to display user’s images from their Instagram account.There’s a matching scene in the Home.storyboard file. Go to Interface Builder for details.
The
See moreHomeVC
class is a subclass of theUIViewController
, and it conforms to theUICollectionViewDelegate
,UICollectionViewDatasource
,UICollectionViewDelegateFlowLayout
protocol.Declaration
Swift
class HomeVC : UIViewController
-
The purpose of the
SideMenu
view controller is to provide navigation options.There’s a matching scene in the Home.storyboard file. Go to Interface Builder for details.
The
See moreSideMenu
class is a subclass of theUIViewController
, and it conforms to theUITableViewDatasource
,UITableViewDelegate
protocol.Declaration
Swift
class SideMenu : UIViewController
-
Undocumented
See moreDeclaration
Swift
class SideMenuNavigationController : UISideMenuNavigationController
-
Undocumented
See moreDeclaration
Swift
public class Low_resolution
-
Undocumented
See moreDeclaration
Swift
public class Pagination
-
Undocumented
See moreDeclaration
Swift
public class Standard_resolution
-
The purpose of ‘SideMenuCell’ tableview cell is to configure tableview cell.
There’s matching scene in the SideMenuCell.xib file. Go to Interface Builder for details.
The ‘SideMenuCell’ class is a subclass of the ‘UITableViewCell’.
See moreDeclaration
Swift
class SideMenuCell : UITableViewCell
-
The purpose of ‘SideMenuHeader’ view is to configure side menu header.
There’s matching scene in the SideMenuHeader.xib file. Go to Interface Builder for details.
The ‘SideMenuHeader’ class is a subclass of the ‘UIView’.
See moreDeclaration
Swift
class SideMenuHeader : UIView
-
The purpose of ‘UserCollectionViewCell’ collectionview cell is to configure collection cell.
There’s matching scene in the UserCollectionViewCell.xib file. Go to Interface Builder for details.
The ‘UserCollectionViewCell’ class is a subclass of the ‘UICollectionViewCell’.
See moreDeclaration
Swift
class UserCollectionViewCell : UICollectionViewCell, CellInterface
-
Undocumented
See moreDeclaration
Swift
class NetworkManager
-
Undocumented
See moreDeclaration
Swift
open class Localize : NSObject


Information
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This track displays maps of chromatin state generated by the Broad/MGH ENCODE group using ChIP-seq. Chemical modifications (methylation, acetylation) to the histone proteins present in chromatin influence gene expression by changing how accessible the chromatin is to transcription.
The ChIP-seq method involves first using formaldehyde to cross-link histones and other DNA-associated proteins to genomic DNA within cells. The cross-linked chromatin is subsequently extracted, mechanically sheared, and immunoprecipitated using specific antibodies. After reversal of cross-links, the immunoprecipitated DNA is sequenced and mapped to the human reference genome. The relative enrichment of each antibody-target (epitope) across the genome is inferred from the density of mapped fragments.
This track is a multi-view composite track that contains multiple data types (views). For each view, there are multiple subtracks that display individually on the browser. Instructions for configuring multi-view tracks are here. ENCODE tracks typically contain one or more of the following views:
Peaks and signals displayed in this track are the results of pooled replicates. The raw sequence and alignment files for each replicate are available for download.
Metadata for a particular subtrack can be found by clicking the down arrow in the list of subtracks.
ChIP-seq: Cells were grown according to the approved ENCODE cell culture protocols. Cells were fixed in 1% formaldehyde and resuspended in lysis buffer. Chromatin was sheared to 200-700 bp using a Diagenode Bioruptor. Solubilized chromatin was immunoprecipitated with antibodies against each of the histone antibodies listed above. Antibody-chromatin complexes were pulled down using protein A-sepharose (or anti-IgM-conjugated agarose for RNA polymerase II), washed and then eluted. After cross-link reversal and proteinase K treatment, immunoprecipitated DNA was extracted with phenol-chloroform, ethanol precipitated, treated with RNAse and purified. A quantity of 1-10 ng of DNA was end-repaired, adapter-ligated and sequenced by Illumina Genome Analyzers as recommended by the manufacturer.
Alignment: Sequence reads from each IP experiment were aligned to the
human reference genome (GRCh37/hg19) using
MAQ
with default parameters, except '-C 11
' and
'-H output_file
' were added. These options output up to 11 additional best matches
for each read (if any are found) to a file. This information was used to filter
out any read that had more than 10 best matches on the genome. Note that it is
likely that instances where multiple reads align to the same position and with
the same orientation are due to enhanced PCR amplification of a single DNA
fragment. No attempt has been made, however, to remove such artifacts from the
data, following ENCODE practices.
Signal: Fragment densities were computed by counting the number of
reads overlapping each 25 bp bin along the genome. Densities were computed using
igvtools count
with default parameters (in particular, '-w 25
' to set window size
of 25 bp and '-f mean
' to report the mean value across the window),
except for '-e
' which was set to extend the reads to 200 bp, and the
.wig output was converted to bigWig using wigToBigWig
from the UCSC Kent software package.
Peaks: Discrete intervals of ChIP-seq fragment enrichment were
identified using Scripture,
a scan statistics approach, under the assumption of uniform background signal.
All data sets were processed with '-task chip
', and with
'-windows 100,200,500,1000,5000,10000,100000
' (no mask file nor
the '-trim
' option have been used). The resulting called segments
were then further filtered to remove intervals that were significantly enriched
only because they contain smaller enriched intervals within them. This
post-processing step has been implemented using Matlab
. The use of the
post-processing step allowed very large enriched intervals (of the order of
Mbps for H3K27me3, for instance) to be detected, as well as much smaller
intervals, without the need to tailor the parameters of Scripture
based on prior expectations.
This is Release 3 (Aug 2012). It contains 83 new experiments including 6 new cell lines and 25 new antibodies. Please note that an antibody previously labeled "Pol2 (b)" is, in fact, Covance antibody MMS-128P with the target POLR2A.
The ChIP-seq data were generated at the Broad Institute and in the Bernstein lab at the Massachusetts General Hospital/Harvard Medical School.
Data generation and analysis were supported by funds from the NHGRI, the Burroughs Wellcome Fund, Massachusetts General Hospital and the Broad Institute.
Contact: Noam ShoreshBernstein BE, Kamal M, Lindblad-Toh K, Bekiranov S, Bailey DK, Huebert DJ, McMahon S, Karlsson EK, Kulbokas EJ 3rd, Gingeras TR et al. Genomic maps and comparative analysis of histone modifications in human and mouse. Cell. 2005 Jan 28;120(2):169-81.
Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K et al. A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell. 2006 Apr 21;125(2):315-26.
Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, Zhang X, Wang L, Issner R, Coyne M et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011 May 5;473(7345):43-9.
Guttman M, Garber M, Levin JZ, Donaghey J, Robinson J, Adiconis X, Fan L, Koziol MJ, Gnirke A, Nusbaum C et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol. 2010 May;28(5):503-10.
Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, Koche RP et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature. 2007 Aug 2;448(7153):553-60.
Ram O, Goren A, Amit I, Shoresh N, Yosef N, Ernst J, Kellis M, Gymrek M, Issner R, Coyne M et al. Combinatorial patterning of chromatin regulators uncovered by genome-wide location analysis in human cells. Cell. 2011 Dec 23;147(7):1628-39.
Data users may freely use ENCODE data, but may not, without prior consent, submit publications that use an unpublished ENCODE dataset until nine months following the release of the dataset. This date is listed in the Restricted Until, above. The full data release policy for ENCODE is available here.


A tool to help you calculate how many drivers you need so everyone gets enough seat time but doesn't have a cry about not having enough money. Prices taken from http://24lemons.com.au/cough-up-or-shut-up-2/. Yeah, it's not rocket science working out cost but this makes it easier.
I'm not affiliated with 24hrs of LeMons, I was just thinking of entering and thought this would answer a question that a lot of people will ask. View the code for this at https://github.com/tomsaleeba/24hrslemonscostcalc.
| Number of drivers | Cost per driver | Drive/seat time |
|---|---|---|
| {{curr.drivers}} | ${{curr.cost | number:2}} | {{curr.seatTime | number:1}}hrs |


SecureSocial Activator Template
This is an application to see how to use Play 2/Scala, Slick and SecureSocial together. It uses Slick 2.0.0 and an in memory H2 database by default.
SecureSocial is an authentication plugin on Play that allows users to login through a variety of methods, including through OAuth providers.
The blog post is at http://blog.lunatech.com/2013/07/04/play-securesocial-slick
Adding OAuth secret codes
SecureSocial requires keys and secrets to authenticate with OAuth providers.
To add these secrets to your configuration, copy secrets.sample.conf
to secrets.conf
and fill in the blanks.
The secrets.conf
file is included from application.conf, and not from securesocial.conf
, since it also contains the Play application secret.
Configuration of SecureSocial is at http://securesocial.ws/guide/configuration.html
Setup and running
Common setup:
- Copy
conf/secrets.sample.conf
toconf/secrets.conf
- Generate an application secret in
application.secret
(easiest way is to create a new Play app, then copy it) - Set up your mail server or leave
smtp.mock=true
set in secrets.conf (you will have to look at console for output).
To run with Activator:
-
Download Activator or
brew install typesafe-activator
or./activator
activator ui
To run natively:
- Make sure you have Play 2.2.1 installed.
play run
Running with OAuth providers:
Here, we need to use a callback, so we use ngrok to expose the internal service, and don't really need nginx (but it's a good habit to use it).
- Register for any external OAuth services you want to authenticate.
- Put the oauth credentials into
secrets.conf
. - Modify
play.plugins
to use the OAuth plugins you registered (a complete list is inplay.plugins.withoauth
). - Download and install nginx (
brew install nginx
if you're on a Mac). - Download and install ngrok from https://ngrok.com -- this allows OAuth callbacks to happen if need be.
- Start the play server (as above).
- Start ngrok with
ngrok 8080
. - Modify the server_name in
conf/nginx.conf
to point to your ngrok URL (i.e. ei3fawekemf.ngrok.com) - Copy
nginx.conf
to the nginx configuration directory with appropriate file name, i.e. /usr/local/etc/nginx/play.conf - Start nginx with
nginx -c /usr/local/etc/nginx/play.conf
(to stop, usenginx -s stop
). - Go to the public URL of your application, i.e. http://ei3fawekemf.ngrok.com
- Go to Twitter, enter the callback URL as http://ei3fawekemf.ngrok.com/authenticate/twitter
- Try to login with Twitter, etc.
Note that you can also use HTTPS with ngrok and it will be correctly set up with a valid SSL certificate (which makes SSL testing much easier). You can also examine HTTP requests in ngrok by going to http://localhost:4040/.


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Overview¶
Modularity and Extensibility¶
Aqua is a library of quantum algorithms specifically designed in order to be modular and extensible at multiple levels. This allows different users with different levels of experience and scientific interests to contribute to, and extend, the Aqua software stack at different levels.
Input Generation¶
At the application level, Aqua allows for classical computational software to be used as the quantum application front end. This module is extensible; new computational software can be easily plugged in. Behind the scenes, Aqua lets that software perform some initial computations classically. The results of those computations are then combined with the problem configuration and translated into input for one or more quantum algorithms, which invoke the Qiskit Terra code APIs to build, compile and execute quantum circuits.
Input Translation¶
The problem configuration and (if present) the additional intermediate data obtained from the classical execution of the computational software are combined to form the input to the quantum system. This phase, known as translation, is also extensible. Practitioners interested in providing more efficient translation operators may do so by extending this layer of the Aqua software stack with their own translation operator implementation.
Quantum Algorithms¶
Quantum algorithm researchers and developers can experiment with the algorithms already included in Aqua, or contribute their own algorithms via the pluggable interface exposed by Aqua. In addition to plain quantum algorithms, Aqua offers a vast set of supporting components, such as variational forms, local and global optimizers, initial states, Quantum Fourier Transforms (QFTs) and Grover oracles. These components are also extensible via pluggable interfaces.
Novel Features¶
In addition to its modularity and extensibility, ability to span across multiple domains, and top-to-bottom completeness from classical computational software to quantum hardware, compared to other quantum software stacks, Aqua presents numerous unique advantages in terms of usability, functionality, and configuration-correctness enforcement.
User Experience¶
Allowing classical computational software at the frontend has its own important advantages. In fact, at the top of the Aqua software stack are industry-domain experts, who are most likely very familiar with existing computational software specific to their own domains. These practitioners are probably interested in experimenting with the benefits of quantum computing in terms of performance, accuracy and reduction of computational complexity, but at the same time they might be unwilling to learn about the underlying quantum infrastructure. Ideally, such practitioners would like to use the computational software they are familiar with as a front end to the quantum computing system, without having to learn a new quantum programming language or new APIs. It is also likely that such practitioners may have collected, over time, numerous problem configurations, corresponding to various experiments. Aqua has been designed to accept those configuration files with no modifications, and without requiring a practitioner experienced in a particular domain to have to learn a quantum programming language. This approach has a clear advantage in terms of usability.
Functionality¶
If Aqua had been designed to interpose a quantum programming language or new APIs between the user and the classical computational software, it would not have been able to fully exploit all the features of the underlying classical computational software unless those features had been exposed at the higher programming-language or API level. In other words, in order to drive the classical execution of any interfaced computational software to the most precise computation of the intermediate data needed to form the quantum input, the advanced features of that software would have had to be configurable through Aqua. Given the intention for Aqua to have an extensible interface capable of accepting any classical computational software, the insertion of a quantum-specific programming language or API would have been not only a usability obstacle, but also a functionality-limiting factor. The ability of Aqua to directly interface classical computational software allows that software to compute the intermediate data needed to form the quantum input at its highest level of precision.
Configuration Correctness¶
Aqua offers another unique feature. Given that Aqua allows traditional software to be executed on a quantum system, configuring an experiment in a particular domain may require a hybrid configuration that involves both domain- and quantum-specific configuration parameters whose values cannot be assigned in isolation from each other. The chances of introducing configuration errors, making typos, or selecting incompatible configuration parameters are very high, especially for people who are expert in a given domain but new to the realm of quantum computing. To address such issues, in Aqua the problem-specific and quantum-specific configuration data is dynamically verified for correctness so that the combination of classical and quantum inputs is resilient to configuration errors. Very importantly, configuration correctness is dynamically enforced even for components that are dynamically discovered and loaded.
Contributors (in Alphabetical Order)¶
Aqua was inspired, authored and brought about by the collective work of a team of researchers. Aqua continues now to grow with the help and work of many people, listed here in alphabetical order, who contribute to the project at different levels:
- Panagiotis Barkoutsos
- Sergey Bravyi
- Chun-Fu (Richard) Chen
- Antonio Córcoles-Gonzalez
- Jay Gambetta
- Jen Glick
- Tanvi Gujarati
- Shaohan Hu
- Takashi Imamichi
- Tal Kachman
- Peng Liu
- Manoel Marques
- Antonio Mezzacapo
- Nikolaj Moll
- Giacomo Nannicini
- Marco Pistoia
- Julia Rice
- Raymond Harry Putra Rudy
- Ivano Tavernelli
- Stephen Wood
License¶
This project uses the Apache License Version 2.0 software license.


UTC -4.
YACHTING ESSSENTIALS
Electricity
Voltage: 220 V
Frequency: 50 Hz
LPG/Gas
Propane tanks cannot be filled here, only exchanged. You may have to buy a camping gaz tank and regulator (these are the small blue tanks - 3kg).
Euro.
US$ are sometimes accepted.
Credit cards are widely accepted and most banks have ATMs.
Effective 1 July 2015, the I cent and 2 cent EC coins will be withdrawn from circulation. The coins will no longer be valid either as consumers will not be able to conduct transactions using these coins. Persons can present the coins at any of the commercial banks which operate within the Eastern Caribbean Currency Union (ECCU) to receive face value.
A price rounding system will be implemented within the ECCU countries for the settlement of cash transactions only, whereby the total payment, inclusive of duties, fees and taxes, will be rounded symmetrically to the nearest five cents. Non-cash payments using cheques, debit andcredit cards will not be impacted and therefore will not be rounded.
International Dialing Code for Martinique is +596 (Note that Local code is also 596 or 696 for mobiles)
If you buy a mobile phone sim card in one of the other East Caribbean islands, the supplier (either Digicel or Cable and Wireless) should set it to roaming, otherwise it won't work in Martinique or Guadeloupe.
Digicel update December 2014:
Anyone cruising using Digicel should be aware that if you purchase a SIM card in one country, it cannot be topped up in different one, as previously possible. So if you wish to use the sim bought here while cruising in other countries, you should either put on a large enough sum to cover your needs, or set up an account and do it online.
You can top-up Digicel at https://online-top-up.digicelgroup.com/
Cell phone coverage is available everywhere on Martinique, but you will want to check with your cellular service provider to be sure they offer international coverage and that your cell phone supports service from the caribbean. Check rates.
Telecoms are improving fast but it can prove frustrating when trying to phone home or arrange travel etc.
June Banana's Cup
Sailing Week, early April
Mardi Gras Carnival
Police - Dial 17.
Fire (Pompiers) -Dial 18
Ambulance - Dial 15 or (596-596) 63-33-33
IMRAY AND ADLARD COLES PILOT BOOKS are available at a discounted price for Noonsite.com users via World Cruising Club
A Cruising Guide to the Lesser Antilles 2.0 (Volume III: Windward Islands)
Author: Frank Virgintino
Published by www.freecruisingguide.com (March 2016)
Available at Kindle as well as all other EPub bookstores
A Thinking Mans Guide to Voyages South - the many facets of Caribbean Cruising
Author: Frank Virgintino
Published: 3rd Edition January 2013
Available as a Kindle version at www.amazon.com as well as other EPub bookstores.
The latest edition of this route planner is larger than ever. For more details see this report
2013-2014 Sailors Guide to the Windward Islands: Martinique to Grenada
By Chris Doyle
Publisher: Cruising Publications (16th Edition, Nov 2012)
ISBN:978-0-944428-94-8
This guide features detailed sketch charts based on the author's own surveys, and aerial photos of most anchorages with clear and concise navigational information. Covers the islands from Martinique to Grenada, with dazzling scenic photography, unsurpassed onshore information, sections on exploring, provisioning, water sports, services, restaurants and photography. Information is linked to the author's website where you can download town maps, GPS waypoints from the sketch charts, and obtain links to local weather, news and more.
Street's Guide Martinique to Trinidad
By D M Street
Publisher: iUniverse.com Inc(March 2001)
ISBN:<PHONE_NUMBER>563
These guides give the mariner all the information needed to safely cruise the area, not only piloting information but also interesting background information on people, places and history
Routes and Waypoints
By Captain Pieter D. Brown
Publisher Baico Publishing Consultants Inc., Ontario, Canada (2011)
ISBN: 978-1-926596-83-9
A Navigational & Piloting Guide of Suggested Routes with Waypoints from Eastern Canada to Grenada including: the Canadian & USA E. Coasts, Bahamas, Turks & Caicos, Cuba, Dominican Republic, Puerto Rico, the Virgin Islands, the Leeward & Windward Islands.
Read Review here
Grenada to The Virgin Islands
By Jacques Patuelli
Published by Imray 3rd Edition 2014
ISBN:<PHONE_NUMBER>818
Fully illustrated with plans and photos, many of which are new for this third edition, the guide is packed with interesting and useful background information on the Caribbean; its history, tourism, geography and details on sailing in the islands.
Martinique Tourism Promotion Board
Ti'Ponton
The Sailor's Guide to Martinique - updated 2015.


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