text:
@book{CBellRoberts1984,
  title = {Social Researching: Politics, Problems, Practice},
  isbn = {978-0-7100-9884-9},
  shorttitle = {Social Researching},
  author = {Bell, Colin and Roberts, Helen},
  year = {1984},
  keywords = {Social sciences -- Research}
}

@book{RKitchin2014,
  title = {The {{Data Revolution}}: {{Big Data}}, {{Open Data}}, {{Data Infrastructures}} and {{Their Consequences}}},
  isbn = {978-1-4739-0826-0},
  shorttitle = {The {{Data Revolution}}},
  publisher = {{SAGE Publications}},
  author = {Kitchin, Rob},
  year = {2014}
}

@article{RBurrowsGane2006,
  title = {Geodemographics, {{Software}} and {{Class}}},
  volume = {40},
  issn = {0038-0385, 1469-8684},
  abstract = {This article examines some of the implications for the sociological analysis of social class of the migration of geodemographic classifications of various sorts into software systems designed to `sort out' people and places. It begins by offering an overview of the history and development of geodemographic classifications. It then argues that such classifications are increasingly becoming embedded in `software sorting' procedures of various sorts, which in turn leads to the prospect of `automated spatiality' becoming a common feature of the contemporary constitution of social class.},
  language = {en},
  number = {5},
  urldate = {2019-11-08},
  journal = {Sociology},
  url = {http://journals.sagepub.com/doi/10.1177/0038038506067507},
  doi = {10.1177/0038038506067507},
  author = {Burrows, Roger and Gane, Nicholas},
  month = oct,
  year = {2006},
  pages = {793-812},
  file = {C\:\\Users\\presc\\Zotero\\storage\\IJ52DCGU\\Burrows and Gane - 2006 - Geodemographics, Software and Class.pdf;C\:\\Users\\presc\\Zotero\\storage\\S4J4Z2GZ\\Burrows and Gane - 2006 - Geodemographics, Software and Class.pdf},
  ids = {BurrowsGane2006a}
}

@book{TButlerRobson2003,
  title = {London Calling : The Middle Classes and the Re-Making of Inner {{London}}},
  isbn = {978-1-85973-628-9},
  shorttitle = {London Calling},
  abstract = {Marking out the middle classes in London -- Gentrification: a middle-class coping strategy? -- Spatial difference and strategic middle-class activity in the city -- Mapping the neighbourhoods -- A class in and for itself? -- Home and household -- Children, schooling and social reproduction -- Twenty-first century gentrification -- Conclusions -- Bibliography.},
  publisher = {{Berg}},
  author = {Butler, Tim and Robson, Garry},
  year = {2003},
  keywords = {Gentrification -- England -- London -- History -- 20th century,Middle class -- England -- London -- History -- 20th century}
}

@article{SParkerEtAl2007,
  title = {Class Places and Place Classes: {{Geodemographics}} and the Spatialization of Class},
  volume = {10},
  issn = {1369-118X},
  shorttitle = {Class Places and Place Classes},
  abstract = {This paper argues that the 'spatial turn' in the sociology of class - the clustering of people with a similar habitus into what we might think of as 'class places' - is connected in a number of important ways with the ongoing informatization of place, particularly as manifest in the urban informatics technology of geodemographics. This is a technology concerned with the development of the classification of places to commercial and policy ends - the assigning of postcodes to a set of mutually exclusive and exhaustive categories, or 'place classes'. What interests the authors is the manner in which there is a strong concordance between the conclusions of academic sociologists working on the spatialization of class and those of - what might be thought of as - 'commercial sociologists' working in the geodemographics industry. Although the conceptual argot is very different, both have in common an interest in the codification and spatial mapping of habitus, and both arrive at very similar substantive conclusions about contemporary processes of sociocultural spatial clustering. But the authors' interest is not just in the observation that there is an analytic convergence in academic and commercial concerns with the relationship between 'class places' and 'place classes'; rather, it is in their possible co-construction. They argue that geodemographic classifications are not only sociologically important phenomena but also represent an interesting example of a new form of software-mediated recursive urban ontology.},
  language = {en},
  urldate = {2019-11-11},
  journal = {Information, Communication and Society},
  url = {http://dx.doi.org/10.1080/13691180701751122},
  author = {Parker, S. and Uprichard, E. and Burrows, R.},
  month = dec,
  year = {2007},
  pages = {901-920},
  file = {C\:\\Users\\presc\\Zotero\\storage\\IKLHB27C\\6333.html}
}

@book{MvanHamEtAl2012,
  series = {Online Access with Purchase: {{Springer}}},
  title = {Neighbourhood Effects Research. New Perspectives},
  isbn = {978-94-007-2309-2},
  publisher = {{Springer Science+Business Media B.V.}},
  author = {{van Ham}, Maarten and Manley, David and of urban studies){\aftergroup\ignorespaces} Bailey, Nick (Professor and Simpson, Ludi and Maclennan, Duncan},
  year = {2012},
  keywords = {Neighborhoods}
}

@article{MSavageBurrows2007,
  title = {The {{Coming Crisis}} of {{Empirical Sociology}}},
  volume = {41},
  issn = {0038-0385},
  abstract = {This ar ticle argues that in an age of knowing capitalism, sociologists have not adequately thought about the challenges posed to their expertise by the proliferation of `social' transactional data which are now routinely collected, processed and analysed by a wide variety of private and public institutions. Drawing on British examples, we argue that whereas over the past 40 years sociologists championed innovative methodological resources, notably the sample survey and the in-depth interviews, which reasonably allowed them to claim distinctive expertise to access the `social' in powerful ways, such claims are now much less secure. We argue that both the sample survey and the in-depth interview are increasingly dated research methods, which are unlikely to provide a robust base for the jurisdiction of empirical sociologists in coming decades. We conclude by speculating how sociology might respond to this coming crisis through taking up new interests in the `politics of method'.},
  language = {en},
  number = {5},
  urldate = {2019-11-09},
  journal = {Sociology},
  url = {https://doi.org/10.1177/0038038507080443},
  doi = {10.1177/0038038507080443},
  author = {Savage, Mike and Burrows, Roger},
  month = oct,
  year = {2007},
  pages = {885-899},
  file = {C\:\\Users\\presc\\Zotero\\storage\\2YYNFWBS\\Savage and Burrows - 2007 - The Coming Crisis of Empirical Sociology.pdf}
}

@book{PBourdieu2013,
  series = {Routledge {{Classics}}},
  title = {Distinction. {{A Social Critique}} of the {{Judgement}} of {{Taste}}},
  isbn = {978-1-135-87316-5},
  abstract = {Summary: No judgement of taste is innocent - we are all snobs. Pierre Bourdieu's Distinction brilliantly illuminates the social pretentions of the middle classes in the modern world, focusing on the tastes and preferences of the French bourgeoisie. First published in 1979, the book is at once a vast ethnography of contemporary France and a dissection of the bourgeois mind. In the course of everyday life we constantly choose between what we find aesthetically pleasing, and what we consider tacky, merely trendy, or ugly. Taste is not pure. Bourdieu demonstrates that our different aesthetic choices are al.},
  publisher = {{Taylor and Francis}},
  author = {Bourdieu, Pierre},
  year = {2013},
  keywords = {Aesthetics; French,France -- Civilization -- 1945-,Social classes -- France}
}

@book{DLyon2003,
  address = {{London ; New York}},
  title = {Surveillance as Social Sorting: Privacy, Risk, and Digital Discrimination},
  isbn = {978-0-415-27872-0 978-0-415-27873-7},
  lccn = {JC596 .S796 2003},
  shorttitle = {Surveillance as Social Sorting},
  publisher = {{Routledge}},
  editor = {Lyon, David},
  year = {2003},
  keywords = {Closed-circuit television,Data protection,Electronic surveillance,Privacy; Right of,Social aspects,Social control},
  note = {OCLC: ocm49952428}
}

@article{JGoss1995,
  title = {"{{We Know Who You Are}} and {{We Know Where You Live}}": {{The Instrumental Rationality}} of {{Geodemographic Systems}}},
  volume = {71},
  issn = {0013-0095},
  shorttitle = {"{{We Know Who You Are}} and {{We Know Where You Live}}"},
  abstract = {This paper provides a critique of geodemographic systems, sophisticated marketing tools that combine massive electronic data bases on consumer characteristics and behavior, segmentation schemes, and Geographic Information Systems (GIS). Responsible for a "revolution" in marketing research, geodemographics represents a strategy to exercise rational knowledge-power over everday life. This critique examines the strategic implications of each component of geodemographics, including electronic surveillance and the erosion of privacy; GIS, spatial inference, and the representation of social space; and segmentation and construction of consumer identity. The paper concludes with remarks about the role of the consumer in geodemographics and the potential for tactical resistances to its strategy.},
  number = {2},
  urldate = {2019-11-08},
  journal = {Economic Geography},
  url = {http://www.jstor.org/stable/144357},
  doi = {10.2307/144357},
  author = {Goss, Jon},
  year = {1995},
  pages = {171-198},
  file = {C\:\\Users\\presc\\Zotero\\storage\\AA24TBHZ\\Goss - 1995 - We Know Who You Are and We Know Where You Live .pdf}
}

@book{JPickles1995,
  address = {{New York}},
  edition = {1 edition},
  title = {Ground {{Truth}}: {{The Social Implications}} of {{Geographic Information Systems}}},
  isbn = {978-0-89862-295-9},
  shorttitle = {Ground {{Truth}}},
  abstract = {This text addresses the impact GIS is having in our social organization and the ways in which GIS is transforming how we think about the discipline of geography. examining the new technologies for gathering, analyzing and mapping geographic data, the author addresses issues about the use of GIS in research, technology, power, democratization and the changing visions of what constitutes market, territory and Empire.},
  language = {English},
  publisher = {{Guilford Press}},
  editor = {Pickles, John},
  month = feb,
  year = {1995}
}

@book{RWebberBurrows2018,
  address = {{Los Angeles}},
  title = {The Predictive Postcode: The Geodemographic Classification of {{British}} Society},
  isbn = {978-1-5264-0233-2 978-1-5264-0234-9},
  lccn = {HB2043.A3 W43 2018},
  shorttitle = {The Predictive Postcode},
  abstract = {"It is not lost on commercial organisations that where we live colours how we view ourselves and others. That is why so many now place us into social groups on the basis of the type of postcode in which we live. Social scientists call this practice commercial sociology . Richard Webber originated Acorn and Mosaic, the two most successful geodemographic classifications. Roger Burrows is a critical interdisciplinary social scientist. Together they chart the origins of this practice and explain the challenges it poses to long-established social scientific beliefs such as: the role of the questionnaire in an era of big data the primacy of theory the relationship between qualitative and quantitative modes of understanding the relevance of visual clues to lay understanding. To help readers evaluate the validity of this form of classification, the book assesses how well geodemographic categories track the emergence of new types of residential neighbourhood and subject a number of key contemporary issues to geodemographic modes of analysis."--},
  publisher = {{SAGE}},
  author = {Webber, Richard and Burrows, Roger},
  year = {2018},
  keywords = {Human geography,Geodemographics,Neighborhoods,Social aspects,Economic aspects,Environment and Ecology,Great Britain,Postal codes},
  note = {OCLC: on1004841108}
}

@book{EShevkyBell1955,
  title = {Social Area Analysis: Theory, Illustrative Application, and Computational Procedures},
  shorttitle = {Social Area Analysis},
  author = {Shevky, Eshref and Bell, Wendell},
  year = {1955},
  keywords = {California San Francisco Bay Area,San Francisco Bay Area (Calif.),Social conditions,Social surveys}
}

license: Unlicense

repository: peterprescott/citation-graph

repo_name: peterprescott/citation-graph

gha_license_id: Unlicense


text:
package profile

import (
	"testing"
)

func TestNewAdapter1(t *testing.T) {
	t.Log("Create Adapter1")

	a := NewAdapter1("hci0")

	t.Log("Start Discovery")
	err := a.StartDiscovery()
	if err != nil {
		t.Log("Error on StartDiscovery")
		t.Fatal(err)
	}

	t.Log("Stop Discovery")
	err = a.StopDiscovery()
	if err != nil {
		t.Log("Error on StartDiscovery")
		t.Fatal(err)
	}

	t.Skipped()

}

license: MIT

repository: saurabh-newera/BLE

license_type: permissive

repo_name: saurabh-newera/BLE

branch_name: refs/heads/master

extension: go


text:
@InProceedings{jiang2012scalable,
author="Jiang, Xueyan
and Tresp, Volker
and Huang, Yi
and Nickel, Maximilian
and Kriegel, Hans-Peter",
editor="Flach, Peter A.
and De Bie, Tijl
and Cristianini, Nello",
title="Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information",
booktitle="Machine Learning and Knowledge Discovery in Databases",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="601--616",
abstract="We consider the problem of predicting instantiated binary relations in a multi-relational setting and exploit both intrarelational correlations and contextual information. For the modular combination we discuss simple heuristics, additive models and an approach that can be motivated from a hierarchical Bayesian perspective. In the concrete examples we consider models that exploit contextual information both from the database and from contextual unstructured information, e.g., information extracted from textual documents describing the involved entities. By using low-rank approximations in the context models, the models perform latent semantic analyses and can generalize across specific terms, i.e., the model might use similar latent representations for semantically related terms. All the approaches we are considering have unique solutions. They can exploit sparse matrix algebra and are thus highly scalable and can easily be generalized to new entities. We evaluate the effectiveness of nonlinear interaction terms and reduce the number of terms by applying feature selection. For the optimization of the context model we use an alternating least squares approach. We experimentally analyze scalability. We validate our approach using two synthetic data sets and using two data sets derived from the Linked Open Data (LOD) cloud.",
isbn="978-3-642-33460-3"
}

repository: mnick/gh-pages

repo_name: mnick/gh-pages

extension: bib


text:
// Copyright 2018 Google LLC. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

package nftables

import (
	"errors"
	"fmt"
	"sync"

	"github.com/google/nftables/binaryutil"
	"github.com/google/nftables/expr"
	"github.com/mdlayher/netlink"
	"github.com/mdlayher/netlink/nltest"
	"golang.org/x/sys/unix"
)

// A Conn represents a netlink connection of the nftables family.
//
// All methods return their input, so that variables can be defined from string
// literals when desired.
//
// Commands are buffered. Flush sends all buffered commands in a single batch.
type Conn struct {
	TestDial nltest.Func // for testing only; passed to nltest.Dial
	NetNS    int         // fd referencing the network namespace netlink will interact with.

	lasting  bool       // establish a lasting connection to be used across multiple netlink operations.
	mu       sync.Mutex // protects the following state
	messages []netlink.Message
	err      error
	nlconn   *netlink.Conn // netlink socket using NETLINK_NETFILTER protocol.
}

// ConnOption is an option to change the behavior of the nftables Conn returned by Open.
type ConnOption func(*Conn)

// New returns a netlink connection for querying and modifying nftables. Some
// aspects of the new netlink connection can be configured using the options
// WithNetNSFd, WithTestDial, and AsLasting.
//
// A lasting netlink connection should be closed by calling CloseLasting() to
// close the underlying lasting netlink connection, cancelling all pending
// operations using this connection.
func New(opts ...ConnOption) (*Conn, error) {
	cc := &Conn{}
	for _, opt := range opts {
		opt(cc)
	}

	if !cc.lasting {
		return cc, nil
	}

	nlconn, err := cc.dialNetlink()
	if err != nil {
		return nil, err
	}
	cc.nlconn = nlconn
	return cc, nil
}

// AsLasting creates the new netlink connection as a lasting connection that is
// reused across multiple netlink operations, instead of opening and closing the
// underlying netlink connection only for the duration of a single netlink
// operation.
func AsLasting() ConnOption {
	return func(cc *Conn) {
		// We cannot create the underlying connection yet, as we are called
		// anywhere in the option processing chain and there might be later
		// options still modifying connection behavior.
		cc.lasting = true
	}
}

// WithNetNSFd sets the network namespace to create a new netlink connection to:
// the fd must reference a network namespace.
func WithNetNSFd(fd int) ConnOption {
	return func(cc *Conn) {
		cc.NetNS = fd
	}
}

// WithTestDial sets the specified nltest.Func when creating a new netlink
// connection.
func WithTestDial(f nltest.Func) ConnOption {
	return func(cc *Conn) {
		cc.TestDial = f
	}
}

// netlinkCloser is returned by netlinkConn(UnderLock) and must be called after
// being done with the returned netlink connection in order to properly close
// this connection, if necessary.
type netlinkCloser func() error

// netlinkConn returns a netlink connection together with a netlinkCloser that
// later must be called by the caller when it doesn't need the returned netlink
// connection anymore. The netlinkCloser will close the netlink connection when
// necessary. If New has been told to create a lasting connection, then this
// lasting netlink connection will be returned, otherwise a new "transient"
// netlink connection will be opened and returned instead. netlinkConn must not
// be called while the Conn.mu lock is currently helt (this will cause a
// deadlock). Use netlinkConnUnderLock instead in such situations.
func (cc *Conn) netlinkConn() (*netlink.Conn, netlinkCloser, error) {
	cc.mu.Lock()
	defer cc.mu.Unlock()
	return cc.netlinkConnUnderLock()
}

// netlinkConnUnderLock works like netlinkConn but must be called while holding
// the Conn.mu lock.
func (cc *Conn) netlinkConnUnderLock() (*netlink.Conn, netlinkCloser, error) {
	if cc.nlconn != nil {
		return cc.nlconn, func() error { return nil }, nil
	}
	nlconn, err := cc.dialNetlink()
	if err != nil {
		return nil, nil, err
	}
	return nlconn, func() error { return nlconn.Close() }, nil
}

func receiveAckAware(nlconn *netlink.Conn, sentMsgFlags netlink.HeaderFlags) ([]netlink.Message, error) {
	if nlconn == nil {
		return nil, errors.New("netlink conn is not initialized")
	}

	// first receive will be the message that we expect
	reply, err := nlconn.Receive()
	if err != nil {
		return nil, err
	}

	if (sentMsgFlags & netlink.Acknowledge) == 0 {
		// we did not request an ack
		return reply, nil
	}

	if (sentMsgFlags & netlink.Dump) == netlink.Dump {
		// sent message has Dump flag set, there will be no acks
		// https://github.com/torvalds/linux/blob/7e062cda7d90543ac8c7700fc7c5527d0c0f22ad/net/netlink/af_netlink.c#L2387-L2390
		return reply, nil
	}

	// Dump flag is not set, we expect an ack
	ack, err := nlconn.Receive()
	if err != nil {
		return nil, err
	}

	if len(ack) == 0 {
		return nil, errors.New("received an empty ack")
	}

	msg := ack[0]
	if msg.Header.Type != netlink.Error {
		// acks should be delivered as NLMSG_ERROR
		return nil, fmt.Errorf("expected header %v, but got %v", netlink.Error, msg.Header.Type)
	}

	if binaryutil.BigEndian.Uint32(msg.Data[:4]) != 0 {
		// if errno field is not set to 0 (success), this is an error
		return nil, fmt.Errorf("error delivered in message: %v", msg.Data)
	}

	return reply, nil
}

// CloseLasting closes the lasting netlink connection that has been opened using
// AsLasting option when creating this connection. If either no lasting netlink
// connection has been opened or the lasting connection is already in the
// process of closing or has been closed, CloseLasting will immediately return
// without any error.
//
// CloseLasting will terminate all pending netlink operations using the lasting
// connection.
//
// After closing a lasting connection, the connection will revert to using
// on-demand transient netlink connections when calling further netlink
// operations (such as GetTables).
func (cc *Conn) CloseLasting() error {
	// Don't acquire the lock for the whole duration of the CloseLasting
	// operation, but instead only so long as to make sure to only run the
	// netlink socket close on the first time with a lasting netlink socket. As
	// there is only the New() constructor, but no Open() method, it's
	// impossible to reopen a lasting connection.
	cc.mu.Lock()
	nlconn := cc.nlconn
	cc.nlconn = nil
	cc.mu.Unlock()
	if nlconn != nil {
		return nlconn.Close()
	}
	return nil
}

// Flush sends all buffered commands in a single batch to nftables.
func (cc *Conn) Flush() error {
	cc.mu.Lock()
	defer func() {
		cc.messages = nil
		cc.mu.Unlock()
	}()
	if len(cc.messages) == 0 {
		// Messages were already programmed, returning nil
		return nil
	}
	if cc.err != nil {
		return cc.err // serialization error
	}
	conn, closer, err := cc.netlinkConnUnderLock()
	if err != nil {
		return err
	}
	defer func() { _ = closer() }()

	if _, err := conn.SendMessages(batch(cc.messages)); err != nil {
		return fmt.Errorf("SendMessages: %w", err)
	}

	// Fetch the requested acknowledgement for each message we sent.
	for _, msg := range cc.messages {
		if msg.Header.Flags&netlink.Acknowledge == 0 {
			continue // message did not request an acknowledgement
		}
		if _, err := conn.Receive(); err != nil {
			return fmt.Errorf("conn.Receive: %w", err)
		}
	}

	return nil
}

// FlushRuleset flushes the entire ruleset. See also
// https://wiki.nftables.org/wiki-nftables/index.php/Operations_at_ruleset_level
func (cc *Conn) FlushRuleset() {
	cc.mu.Lock()
	defer cc.mu.Unlock()
	cc.messages = append(cc.messages, netlink.Message{
		Header: netlink.Header{
			Type:  netlink.HeaderType((unix.NFNL_SUBSYS_NFTABLES << 8) | unix.NFT_MSG_DELTABLE),
			Flags: netlink.Request | netlink.Acknowledge | netlink.Create,
		},
		Data: extraHeader(0, 0),
	})
}

func (cc *Conn) dialNetlink() (*netlink.Conn, error) {
	if cc.TestDial != nil {
		return nltest.Dial(cc.TestDial), nil
	}

	return netlink.Dial(unix.NETLINK_NETFILTER, &netlink.Config{NetNS: cc.NetNS})
}

func (cc *Conn) setErr(err error) {
	if cc.err != nil {
		return
	}
	cc.err = err
}

func (cc *Conn) marshalAttr(attrs []netlink.Attribute) []byte {
	b, err := netlink.MarshalAttributes(attrs)
	if err != nil {
		cc.setErr(err)
		return nil
	}
	return b
}

func (cc *Conn) marshalExpr(fam byte, e expr.Any) []byte {
	b, err := expr.Marshal(fam, e)
	if err != nil {
		cc.setErr(err)
		return nil
	}
	return b
}

func batch(messages []netlink.Message) []netlink.Message {
	batch := []netlink.Message{
		{
			Header: netlink.Header{
				Type:  netlink.HeaderType(unix.NFNL_MSG_BATCH_BEGIN),
				Flags: netlink.Request,
			},
			Data: extraHeader(0, unix.NFNL_SUBSYS_NFTABLES),
		},
	}

	batch = append(batch, messages...)

	batch = append(batch, netlink.Message{
		Header: netlink.Header{
			Type:  netlink.HeaderType(unix.NFNL_MSG_BATCH_END),
			Flags: netlink.Request,
		},
		Data: extraHeader(0, unix.NFNL_SUBSYS_NFTABLES),
	})

	return batch
}

repository: google/nftables

repo_name: google/nftables

branch_name: refs/heads/main

gha_language: Go


text:
#------------------------------------------------------------------------------#
# Copyright (c) 2014 Los Alamos National Security, LLC
# All rights reserved.
#------------------------------------------------------------------------------#

#------------------------------------------------------------------------------#
# Add option to enable MPI
#------------------------------------------------------------------------------#

option(ENABLE_MPI "Enable MPI" OFF)
option(ENABLE_MPI_CXX_BINDINGS "Enable MPI C++ Bindings" OFF)
option(ENABLE_MPI_THREAD_MULITPLE "Enable MPI_THREAD_MULTIPLE" OFF)
mark_as_advanced(ENABLE_MPI_THREAD_MULITPLE)

if(ENABLE_MPI)

#------------------------------------------------------------------------------#
# Find MPI
#------------------------------------------------------------------------------#

find_package(MPI COMPONENTS C CXX REQUIRED)
list(APPEND CINCH_RUNTIME_LIBRARIES MPI::MPI_CXX MPI::MPI_C)

endif(ENABLE_MPI)

#------------------------------------------------------------------------------#
# Formatting options for emacs and vim.
# vim: set tabstop=4 shiftwidth=4 expandtab :
#------------------------------------------------------------------------------#

repository: laristra/cinch

repo_name: laristra/cinch

gha_license_id: NOASSERTION

gha_language: CMake

extension: cmake


text:
% Encoding: UTF-8

@article{Fan1994,
author = {Fan, Jianqing and Marron, James S.},
title = {Fast Implementations of Nonparametric Curve Estimators},
journal = {Journal of Computational and Graphical Statistics},
volume = {3},
number = {1},
pages = {35-56},
year  = {1994}
}

@article{Brave2014,
	author  = {Brave, S. and Walstrum, T.},
	year    = {2014},
	title   = {Estimating marginal treatment effects using parametric and semiparametric methods},
	journal = {Stata Journal},
	volume  = {14},
	number  = {1},
	pages   = {191--217},
}

@Article{RoRu1983,
  author  = {Rosenbaum, Paul R. and Donald B. Rubin},
  title   = {The Central Role of the Propensity Score in Observational Studies for Causal Effects},
  journal = {Biometrika},
  year    = {1983},
  volume  = {70},
  number  = {1},
  pages   = {41-55},
}

@Article{Roy1951,
  author  = {Roy, Andrew D.},
  title   = {Some Thoughts on the Distribution of Earnings},
  journal = {Oxford Economic Papers},
  year    = {1951},
  volume  = {3},
  number  = {2},
  pages   = {135-146},
}

@Article{Quandt1958,
  author  = {Quandt, Richard E.},
  title   = {The Estimation of the Parameters of a Linear Regression System Obeying Two Seperate Regimes.},
  journal = {Journal of the American Statistical Association},
  year    = {1958},
  volume  = {53},
  number  = {284},
  pages   = {873-880},
}

@Article{Quandt1972,
  author  = {Quandt, Richard E.},
  title   = {A New Approach to Estimating Switching Regressions.},
  journal = {Journal of the American Statistical Association},
  year    = {1972},
  volume  = {67},
  number  = {338},
  pages   = {306-310},
}

@Article{HSMV97,
  author  = {Horwitz, Ralph I. and Singer, Burton H. and Makuch, Robert. W. and Viscoli, Catherine M.},
  title   = {Reaching the Tunnel at the End of the Light.},
  journal = {Journal of Clinical Epidemiology},
  year    = {1997},
  volume  = {50},
  number  = {7},
  pages   = {753-755},
}

@Article{HeSmCl97,
  author  = {Heckman, James J. and Smith, Jeffrey and Clements, Nancy},
  title   = {Making the Most Out of Programme Evaluations and Social Experiments: {A}ccounting for Heterogeneity in Programme Impacts},
  journal = {Review of Economic Studies},
  year    = {1997},
  volume  = {64},
  number  = {4},
  pages   = {487-535},
}

@Article{Heckman10,
  author  = {Heckman, James J.},
  title   = {Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy},
  journal = {Journal of Economic Literature},
  year    = {2010},
  volume  = {48},
  number  = {2},
  pages   = {356-398},
}

@Article{Imbens94,
  author  = {Imbens, Guido W. and Angrist, Joshua D.},
  title   = {Identification and Estimation of Local Average Treatment Effects},
  journal = {Econometrica},
  year    = {1994},
  volume  = {62},
  number  = {2},
  pages   = {467-475},
}

@Article{HSMV96,
  author  = {Horwitz, Ralph I. and Singer, Burton H. and Makuch, Robert. W. and Viscoli, Catherine M.},
  title   = {Can Treatment that is Helpful on Average be Harmful to Some Patients? {A} Study of Conflicting Information Needs of Clinical Inquiry and Drug Regulation.},
  journal = {Journal of Clinical Epidemiology},
  year    = {1996},
  volume  = {49},
  number  = {4},
  pages   = {395-400},
}

@Article{Holland86,
  author  = {Holland, Paul W.},
  title   = {Statistics and Causal Inference.},
  journal = {Journal of the American Statistical Association},
  year    = {1986},
  volume  = {81},
  number  = {386},
  pages   = {945-960},
}

@Article{HeUrVy06,
  author  = {Heckman, James J. and Urzua, Sergio and Vytlacil, Edward J.},
  title   = {Understanding Instrumental Variables in Models with Essential Heterogeneity.},
  journal = {Journal of Political Economy},
  year    = {2006},
  volume  = {88},
  number  = {3},
  pages   = {389-432},
}

@Article{HecVyr05,
  author  = {Heckman, James J. and Vytlacil, Edward J.},
  title   = {Structural Equations, Treatment Effects, and Econometric Policy Evaluation},
  journal = {Econometrica},
  year    = {2005},
  volume  = {73},
  number  = {3},
  pages   = {669-739},
}

@Article{CocRub72,
  author  = {Cochran, William G. and Rubin, Donald B.},
  title   = {Controlling Bias in Observational Studies: {A} Review.},
  journal = {Sankhya: The Indian Journal of Statistics Series A},
  year    = {1972},
  volume  = {35},
  number  = {4},
  pages   = {417-446},
}

@Article{Heckman2001,
  author  = {Heckman, James J.},
  title   = {Micro Data, Heterogeneity, and the Evaluation of Public Policy: {N}obel Lecture},
  journal = {Journal of Political Economy},
  year    = {2001},
  volume  = {109},
  number  = {4},
  pages   = {673-748},
}

@Article{HeIcSmTo98,
  author  = {Heckman, James J. and Ichimura, Hidehiko and Smith, Jeffrey and Todd, Petra},
  title   = {Characterizing Selection Bias Using Experimental Data},
  journal = {Journal of Political Economy},
  year    = {1998},
  volume  = {66},
  number  = {5},
  pages   = {1017-1098},
}

@Article{HecVyr01,
  author  = {Heckman, James J. and Vytlacil, Edward J.},
  title   = {Policy-Relevant Treatment Effects},
  journal = {American Economic Review},
  year    = {2001},
  volume  = {91},
  number  = {2},
  pages   = {107-111},
}

@Article{AaHeVy2005,
  author  = {Aakvik, Arild and Heckman, James J. and Vytlacil, Edward J.},
  title   = {Estimating treatment effects for discrete outcomes when responses to treatment vary: {A}n application to {N}orwegian vocational rehabilitation programs},
  journal = {Journal of Econometrics},
  year    = {2005},
  volume  = {124},
  number  = {1-2},
  pages   = {15-51},
}

@Article{BjöMof87,
  author  = {Björklund, Anders M. and Moffitt, Robert},
  title   = {The Estimation of Wage Gains and Welfare Gains in Self-Selection Models},
  journal = {Review of Economics and Statistics},
  year    = {1987},
  volume  = {69},
  number  = {1},
  pages   = {42-49},
}

@InCollection{AbbHec07,
  author    = {Jaap H. Abbring and James J. Heckman},
  title     = {Econometric Evaluation of Social Programs, Part {III}: {D}istributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation},
  booktitle = {Handbook of Econometrics},
  publisher = {Elsevier Science},
  year      = {2007},
  volume    = {6B},
  chapter   = {72},
  pages     = {5145 --5303},
  journal   = {Handbook of Econometrics},
}

@InCollection{BrHecHa07,
  author    = {Martin Browning and Lars Peter Hansen and James J. Heckman},
  title     = {Micro Data and General Equilibrium Models},
  booktitle = {Handbook of Macroeconomics},
  publisher = {Elsevier Science},
  year      = {1999},
  volume    = {1A},
  chapter   = {8},
  pages     = {543--633},
}

@InCollection{HecVyr07a,
  author    = {James J. Heckman and Edward J. Vytlacil},
  title     = {Econometric Evaluation of Social Programs, Part {I}: {C}ausal Models, Structural Models and Econometric Policy Evaluation},
  booktitle = {Handbook of Econometrics},
  publisher = {Elsevier Science},
  year      = {2007},
  volume    = {6B},
  chapter   = {70},
  pages     = {4779--4874},
}

@InCollection{HecVyr07b,
  author    = {James J. Heckman and Edward J. Vytlacil},
  title     = {Econometric Evaluation of Social Programs, Part {II}: {U}sing the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments},
  booktitle = {Handbook of Econometrics},
  publisher = {Elsevier Science},
  year      = {2007},
  volume    = {6B},
  chapter   = {71},
  pages     = {4875--5143},
}

@Article{Eisenhauer2015,
  author  = {Eisenhauer,Philipp and Heckman, James J. and Vytlacil, Edward J.},
  title   = {The Generalized {R}oy Model and the Cost-Benefit Analysis of Social Programs},
  journal = {Journal of Political Economy},
  year    = {2015},
  volume  = {123},
  number  = {2},
  pages   = {413-443},
}

@Article{Eisenhauer2013,
    title   = {Issues in the Econometrics of Policy Evaluation: Explorations Using a Factor Structure Model},
    author  = {Eisenhauer, Philipp},
    journal = {Unpublished Manuscript},
    year    = {2013}
  }

@Article{Carneiro2011,
author = {Carneiro, Pedro and Heckman, James J. and Vytlacil, Edward J.},
title = {Estimating {M}arginal {R}eturns to {E}ducation},
journal = {American Economic Review},
year = {2011},
volume = {101},
number = {6},
Pages = {2754-81}
}

@article{Heckman2010,
author = {James J. Heckman and Daniel Schmierer},
title = {Tests of hypotheses arising in the correlated random coefficient model},
journal = {Economic Modelling},
year = {2010},
volume = {27},
number = "6",
pages =  {1355 - 1367}
}

@incollection{Fortin2011,
title = {Chapter 1 - {D}ecomposition {M}ethods in {E}conomics},
editor = {Orley Ashenfelter and David Card},
booktitle = {Handbook of Labor Economics},
publisher = {Elsevier},
volume = {4},
pages = {1 - 102},
year = {2011},
author = {Nicole Fortin and Thomas Lemieux and Sergio Firpo}
}

@article{Firpo2007,
author = {Firpo Sergio},
title = {Efficient {S}emiparametric {E}stimation of {Q}uantile {T}reatment {E}ffects},
journal = {Econometrica},
volume = {75},
number = {1},
pages = {259-276},
year= {2007}
}

@article{AbAnIm2002,
author = {Abadie Alberto and Angrist Joshua and Imbens Guido},
title = {Instrumental {V}ariables {E}stimates of the {E}ffect of {S}ubsidized {T}raining on the {Q}uantiles of {T}rainee {E}arnings},
journal = {Econometrica},
volume = {70},
number = {1},
pages = {91-117},
year = {2002}
}

@article{Chernozhukov2005,
author = {Chernozhukov Victor and Hansen Christian},
title = {An {IV} {M}odel of {Q}uantile {T}reatment {E}ffects},
journal = {Econometrica},
volume = {73},
number = {1},
pages = {245-261},
year={2005}
}

@Comment{jabref-meta: databaseType:bibtex;}

repository: OpenSourceEconomics/grmpy

repo_name: OpenSourceEconomics/grmpy

gha_license_id: MIT

gha_language: Jupyter Notebook


text:
%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

%% Created for Till Hoffmann at 2016-03-04 22:08:40 +0000

%% Saved with string encoding Unicode (UTF-8)

@article{Marchant2013,
	Abstract = {The kernel method is a nonparametric procedure used to estimate densities with support in R . When nonnegative data are modeled, the classical kernel density estimator presents a bias problem in the neighborhood of zero. Several methods have been developed to reduce this bias, which include the boundary kernel, data transformation and reflection methods. An alternative proposal is to use kernel estimators based on distributions with nonnegative support, as is the case of the Birnbaum--Saunders (BS), gamma, inverse Gaussian and lognormal models. Generalized \{BS\} (GBS) distributions have received considerable attention, due to their properties and their flexibility in modeling different types of data. In this paper, we propose, characterize and implement the kernel method based on \{GBS\} distributions to estimate densities with nonnegative support. In addition, we provide a simple method to choose the corresponding bandwidth. In order to evaluate the performance of these new estimators, we conduct a Monte Carlo simulation study. The obtained results are illustrated by analyzing financial real data. },
	Author = {Carolina Marchant and Karine Bertin and V{\'\i}ctor Leiva and Helton Saulo},
	Date-Added = {2016-03-04 22:08:16 +0000},
	Date-Modified = {2016-03-04 22:08:40 +0000},
	Doi = {10.1016/j.csda.2013.01.013},
	Journal = {Computational Statistics \& Data Analysis},
	Pages = {1--15},
	Title = {Generalized {Birnbaum--Saunders} kernel density estimators and an analysis of financial data},
	Volume = {63},
	Year = {2013},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0167947313000285},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.csda.2013.01.013}}

@article{Malec2014,
	Abstract = {Abstract Standard fixed symmetric kernel-type density estimators are known to encounter problems for positive random variables with a large probability mass close to zero. It is shown that, in such settings, alternatives of asymmetric gamma kernel estimators are superior, but also differ in asymptotic and finite sample performance conditionally on the shape of the density near zero and the exact form of the chosen kernel. Therefore, a refined version of the gamma kernel with an additional tuning parameter adjusted according to the shape of the density close to the boundary is suggested. A data-driven method for the appropriate choice of the modified gamma kernel estimator is also provided. An extensive simulation study compares the performance of this refined estimator to those of standard gamma kernel estimates and standard boundary corrected and adjusted fixed kernels. It is found that the finite sample performance of the proposed new estimator is superior in all settings. Two empirical applications based on high-frequency stock trading volumes and realized volatility forecasts demonstrate the usefulness of the proposed methodology in practice. },
	Author = {Peter Malec and Melanie Schienle},
	Date-Added = {2016-03-04 21:57:14 +0000},
	Date-Modified = {2016-03-04 21:57:30 +0000},
	Doi = {10.1016/j.csda.2013.10.023},
	Journal = {Computational Statistics \& Data Analysis},
	Pages = {57--76},
	Title = {Nonparametric kernel density estimation near the boundary},
	Volume = {72},
	Year = {2014},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0167947313003770},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.csda.2013.10.023}}

@article{Kuruwita2010,
	Abstract = {We propose a modification to the regular kernel density estimation method that use asymmetric kernels to circumvent the spill over problem for densities with positive support. First a pivoting method is introduced for placement of the data relative to the kernel function. This yields a strongly consistent density estimator that integrates to one for each fixed bandwidth in contrast to most density estimators based on asymmetric kernels proposed in the literature. Then a data-driven Bayesian local bandwidth selection method is presented and lognormal, gamma, Weibull and inverse Gaussian kernels are discussed as useful special cases. Simulation results and a real-data example illustrate the advantages of the new methodology. },
	Author = {C.N. Kuruwita and K.B. Kulasekera and W.J. Padgett},
	Date-Added = {2016-03-04 14:29:17 +0000},
	Date-Modified = {2016-03-04 14:29:53 +0000},
	Doi = {10.1016/j.jspi.2010.01.001},
	Journal = {Journal of Statistical Planning and Inference},
	Keywords = {asymmetric kernel},
	Number = {7},
	Pages = {1765--1774},
	Title = {Density estimation using asymmetric kernels and {Bayes} bandwidths with censored data},
	Volume = {140},
	Year = {2010},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0378375810000030},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.jspi.2010.01.001},
	Bdsk-File-1 = {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}}

@misc{SourceCode,
	Author = {Till Hoffmann and Jones, Nick S.},
	Date-Added = {2016-01-05 14:08:39 +0000},
	Date-Modified = {2016-03-04 21:08:04 +0000},
	Title = {GitHub repository ``asymmetric\_kde''},
	Url = {http://bit.ly/1ZwNoli},
	Year = {2016},
	Bdsk-Url-1 = {http://bit.ly/1ZwNoli}}

@book{Howie2008,
	Annote = {Residue for ratio of holomorphic functions (Theorem 8.15 on page 148).

Residue for non-simple poles (Theorem 8.17 on page 150).},
	Author = {John M. Howie},
	Date-Added = {2015-12-16 18:49:14 +0000},
	Date-Modified = {2016-03-04 14:28:10 +0000},
	Keywords = {complex analysis},
	Publisher = {Springer},
	Title = {Complex Analysis},
	Year = {2008},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://amzn.to/1gO0Ax3}}

@article{Jeon2014,
	Author = {Yongho Jeon and Joseph Kim},
	Cites = {Chen2000},
	Date-Added = {2015-12-16 18:49:07 +0000},
	Date-Modified = {2016-01-05 14:50:15 +0000},
	Doi = {10.1016/j.insmatheco.2013.08.009},
	Journal = {Insurance Math. Econom.},
	Keywords = {Density estimation; gamma},
	Number = {3},
	Pages = {569--579},
	Title = {A gamma kernel density estimation for insurance loss data},
	Volume = {53},
	Year = {2014},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.insmatheco.2013.08.009}}

@book{Johnson1994,
	Author = {Norman L. Johnson and Samuel Kotz and N. Balakrishnan},
	Date-Added = {2015-12-16 18:48:52 +0000},
	Date-Modified = {2015-12-16 18:48:52 +0000},
	Publisher = {Wiley},
	Title = {Continuous Univariate Distributions},
	Volume = {1},
	Year = {1994},
	Bdsk-File-1 = {YnBsaXN0MDDUAQIDBAUGJCVYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3ASAAGGoKgHCBMUFRYaIVUkbnVsbNMJCgsMDxJXTlMua2V5c1pOUy5vYmplY3RzViRjbGFzc6INDoACgAOiEBGABIAFgAdccmVsYXRpdmVQYXRoWWFsaWFzRGF0YV8QUi4uLy4uLy4uL2dvb2dsZV9kcml2ZS9wYXBlcnMvSm9obnNvbjE5OTQgLSBDb250aW51b3VzIFVuaXZhcmlhdGUgRGlzdHJpYnV0aW9ucy5wZGbSFwsYGVdOUy5kYXRhTxECJgAAAAACJgACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAA0ImcSkgrAAAAESWWH0pvaG5zb24xOTk0IC0gQ29udGluIzFENDc1RC5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAdR13RGyqIAAAAAAAAAAAAAwADAAAJIAAAAAAAAAAAAAAAAAAAAAZwYXBlcnMAEAAIAADQiZxKAAAAEQAIAADRGyqIAAAAAQAQABEllgAJYWMACTsdAAKT0wACAFdNYWNpbnRvc2ggSEQ6VXNlcnM6AHRpbGxob2ZmbWFubjoAZ29vZ2xlX2RyaXZlOgBwYXBlcnM6AEpvaG5zb24xOTk0IC0gQ29udGluIzFENDc1RC5wZGYAAA4AbAA1AEoAbwBoAG4AcwBvAG4AMQA5ADkANAAgAC0AIABDAG8AbgB0AGkAbgB1AG8AdQBzACAAVQBuAGkAdgBhAHIAaQBhAHQAZQAgAEQAaQBzAHQAcgBpAGIAdQB0AGkAbwBuAHMALgBwAGQAZgAPABoADABNAGEAYwBpAG4AdABvAHMAaAAgAEgARAASAFxVc2Vycy90aWxsaG9mZm1hbm4vZ29vZ2xlX2RyaXZlL3BhcGVycy9Kb2huc29uMTk5NCAtIENvbnRpbnVvdXMgVW5pdmFyaWF0ZSBEaXN0cmlidXRpb25zLnBkZgATAAEvAAAVAAIAE///AACABtIbHB0eWiRjbGFzc25hbWVYJGNsYXNzZXNdTlNNdXRhYmxlRGF0YaMdHyBWTlNEYXRhWE5TT2JqZWN00hscIiNcTlNEaWN0aW9uYXJ5oiIgXxAPTlNLZXllZEFyY2hpdmVy0SYnVHJvb3SAAQAIABEAGgAjAC0AMgA3AEAARgBNAFUAYABnAGoAbABuAHEAcwB1AHcAhACOAOMA6ADwAxoDHAMhAywDNQNDA0cDTgNXA1wDaQNsA34DgQOGAAAAAAAAAgEAAAAAAAAAKAAAAAAAAAAAAAAAAAAAA4g=},
	Bdsk-Url-1 = {http://www.amazon.com/Continuous-Univariate-Distributions-Probability-Statistics/dp/0471584959%3FSubscriptionId%3D0JYN1NVW651KCA56C102%26tag%3Dtechkie-20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0471584959}}

@article{Brown1999,
	Abstract = {The problem of boundary bias is associated with kernel estimation for regression curves with compact support. This paper proposes a simple and unified approach for remedying boundary bias in non-parametric regression, without dividing the compact support into interior and boundary areas and without applying explicitly different smoothing treatments separately. The approach uses the beta family of density functions as kernels. The shapes of the kernels vary according to the position where the curve estimate is made. They are symmetric at the middle of the support interval, and become more and more asymmetric nearer the boundary points. The kernels never put any weight outside the data support interval, and thus avoid boundary bias. The method is a generalization of classical Bernstein polynomials, one of the earliest methods of statistical smoothing. The proposed estimator has optimal mean integrated squared error at an order of magnitude $n^{-4/5}$ , equivalent to that of standard kernel estimators when the curve has an unbounded support.},
	Author = {Brown, Bruce M. and Chen, Song Xi},
	Date-Added = {2015-12-16 18:48:41 +0000},
	Date-Modified = {2016-03-04 16:39:17 +0000},
	Doi = {10.1111/1467-9469.00136},
	Journal = {Scand. J. Stat.},
	Keywords = {Density estimation},
	Number = {1},
	Pages = {47--59},
	Title = {{Beta-Bernstein} Smoothing for Regression Curves with Compact Support},
	Volume = {26},
	Year = {1999},
	Bdsk-File-1 = {YnBsaXN0MDDUAQIDBAUGJCVYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3ASAAGGoKgHCBMUFRYaIVUkbnVsbNMJCgsMDxJXTlMua2V5c1pOUy5vYmplY3RzViRjbGFzc6INDoACgAOiEBGABIAFgAdccmVsYXRpdmVQYXRoWWFsaWFzRGF0YV8QcC4uLy4uLy4uL2dvb2dsZV9kcml2ZS9wYXBlcnMvQnJvd24xOTk5IC0gQmV0YS1CZXJuc3RlaW4gU21vb3RoaW5nIGZvciBSZWdyZXNzaW9uIEN1cnZlcyB3aXRoIENvbXBhY3QgU3VwcG9ydC5wZGbSFwsYGVdOUy5kYXRhTxECgAAAAAACgAACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAA0ImcSkgrAAAAESWWH0Jyb3duMTk5OSAtIEJldGEtQmVyI0U5MzY1OC5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADpNljSKwPsAAAAAAAAAAAAAwADAAAJIAAAAAAAAAAAAAAAAAAAAAZwYXBlcnMAEAAIAADQiZxKAAAAEQAIAADSKvXcAAAAAQAQABEllgAJYWMACTsdAAKT0wACAFdNYWNpbnRvc2ggSEQ6VXNlcnM6AHRpbGxob2ZmbWFubjoAZ29vZ2xlX2RyaXZlOgBwYXBlcnM6AEJyb3duMTk5OSAtIEJldGEtQmVyI0U5MzY1OC5wZGYAAA4AqABTAEIAcgBvAHcAbgAxADkAOQA5ACAALQAgAEIAZQB0AGEALQBCAGUAcgBuAHMAdABlAGkAbgAgAFMAbQBvAG8AdABoAGkAbgBnACAAZgBvAHIAIABSAGUAZwByAGUAcwBzAGkAbwBuACAAQwB1AHIAdgBlAHMAIAB3AGkAdABoACAAQwBvAG0AcABhAGMAdAAgAFMAdQBwAHAAbwByAHQALgBwAGQAZgAPABoADABNAGEAYwBpAG4AdABvAHMAaAAgAEgARAASAHpVc2Vycy90aWxsaG9mZm1hbm4vZ29vZ2xlX2RyaXZlL3BhcGVycy9Ccm93bjE5OTkgLSBCZXRhLUJlcm5zdGVpbiBTbW9vdGhpbmcgZm9yIFJlZ3Jlc3Npb24gQ3VydmVzIHdpdGggQ29tcGFjdCBTdXBwb3J0LnBkZgATAAEvAAAVAAIAE///AACABtIbHB0eWiRjbGFzc25hbWVYJGNsYXNzZXNdTlNNdXRhYmxlRGF0YaMdHyBWTlNEYXRhWE5TT2JqZWN00hscIiNcTlNEaWN0aW9uYXJ5oiIgXxAPTlNLZXllZEFyY2hpdmVy0SYnVHJvb3SAAQAIABEAGgAjAC0AMgA3AEAARgBNAFUAYABnAGoAbABuAHEAcwB1AHcAhACOAQEBBgEOA5IDlAOZA6QDrQO7A78DxgPPA9QD4QPkA/YD+QP+AAAAAAAAAgEAAAAAAAAAKAAAAAAAAAAAAAAAAAAABAA=},
	Bdsk-Url-1 = {http://www.jstor.org/stable/4616540}}

@book{Wand1995,
	Author = {Wand, M. P. and Jones, M. C.},
	Date-Added = {2015-12-16 18:48:31 +0000},
	Date-Modified = {2015-12-16 18:48:31 +0000},
	Keywords = {Density estimation; boundary kernel},
	Publisher = {Chapman \& Hall},
	Title = {Kernel Smoothing},
	Year = {1995},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://amzn.to/1NGAAjZ}}

@article{Scaillet2004,
	Abstract = { This paper introduces two new nonparametric estimators for probability density functions which have support on the non-negative real line. These kernel estimators are based on some inverse Gaussian (IG) and reciprocal inverse Gaussian (RIG) probability density functions used as kernels. We show that they share the same properties as those of gamma kernel estimators: they are free of boundary bias, always non-negative and achieve the optimal rate of convergence for the mean integrated squared error (MISE). Monte Carlo results concerning finite sample properties are reported for different distributions and sample sizes. },
	Annote = {The authors build on the method suggested by Chen but use inverse Gaussian and reciprocal inverse Gaussian kernels. They show that the estimators have optimal convergence properties, are free of boundary bias and are non-negative everywhere.

They suggest that the log-normal kernel is not suitable for kernel density estimation which may be true in the formulation where the variable $x$ is considered a parameter of the kernel rather than the univariate random variable. Because of their formulation following Chen, the estimator is not properly normalised.

They derive a plugin estimator using the log-normal distribution as a reference distribution. They encounter the same problem that the Gamma distribution can only be used as a reference distribution if its parameters satisfy certain inequalities. Their plugin estimator apparently doesn't work very well.},
	Author = {Scaillet, O.},
	Cites = {Chen2000},
	Date-Added = {2015-12-16 18:48:16 +0000},
	Date-Modified = {2016-01-05 14:50:31 +0000},
	Doi = {10.1080/10485250310001624819},
	Journal = {J. Nonparametr. Stat.},
	Keywords = {Density estimation; inverse Gaussian},
	Number = {1-2},
	Pages = {217--226},
	Title = {Density estimation using inverse and reciprocal inverse Gaussian kernels},
	Volume = {16},
	Year = {2004},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1080/10485250310001624819}}

@article{Jin2003,
	Abstract = {In this article we extend the class of non-negative, asymmetric kernel density estimators and propose Birnbaum-Saunders (BS) and lognormal (LN) kernel density functions. The density functions have bounded support on [0,1). Both BS and LN kernel estimators are free of boundary bias, non-negative, with natural varying shape, and achieve the optimal rate of convergence for the mean integrated squared error. We apply BS and LN kernel density estimators to high frequency intraday time duration data. The comparisons are made on several nonparametric kernel density estimators. BS and LN kernels perform better near the boundary in terms of bias reduction.},
	Annote = {The authors build on the method suggested by Chen but use Birnbaum-Saunders and log-normal kernels. They show that the estimators have optimal convergence properties, are free of boundary bias and are non-negative everywhere. Because of their formulation the density is not necessarily properly normalised.

They almost provide plugin estimators but do not follow through by using a reference distribution.},
	Author = {Jin, Xiaodong and Kawczak, Janusz},
	Cites = {Chen2000, Scaillet2004 different version from 2001},
	Date-Added = {2015-12-16 18:48:05 +0000},
	Date-Modified = {2016-03-04 16:39:11 +0000},
	Journal = {Annals of Economics and Finance},
	Keywords = {Density estimation; birnbaum-saunders; lognormal},
	Number = {1},
	Pages = {103--124},
	Title = {{Birnbaum-Saunders} and Lognormal Kernel Estimators for Modelling Durations in High Frequency Financial Data},
	Volume = {4},
	Year = {2003},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://EconPapers.repec.org/RePEc:cuf:journl:y:2003:v:4:i:1:p:103-124}}

@article{Chen2000,
	Author = {Chen, Song Xi},
	Date-Added = {2015-12-16 18:47:56 +0000},
	Date-Modified = {2016-01-05 14:47:27 +0000},
	Doi = {10.1023/A:1004165218295},
	Journal = {Ann. Inst. Statist. Math.},
	Keywords = {Density estimation; gamma},
	Number = {3},
	Pages = {471--480},
	Title = {Probability Density Function Estimation Using Gamma Kernels},
	Volume = {52},
	Year = {2000},
	Bdsk-File-1 = {YnBsaXN0MDDUAQIDBAUGJCVYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3ASAAGGoKgHCBMUFRYaIVUkbnVsbNMJCgsMDxJXTlMua2V5c1pOUy5vYmplY3RzViRjbGFzc6INDoACgAOiEBGABIAFgAdccmVsYXRpdmVQYXRoWWFsaWFzRGF0YV8QZy4uLy4uLy4uL2dvb2dsZV9kcml2ZS9wYXBlcnMvQ2hlbjIwMDAgLSBQcm9iYWJpbGl0eSBEZW5zaXR5IEZ1bmN0aW9uIEVzdGltYXRpb24gVXNpbmcgR2FtbWEgS2VybmVscy5wZGbSFwsYGVdOUy5kYXRhTxECZgAAAAACZgACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAA0ImcSkgrAAAAESWWH0NoZW4yMDAwIC0gUHJvYmFiaWxpIzI4NkE2NS5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAoamXRCkIKAAAAAAAAAAAAAwADAAAJIAAAAAAAAAAAAAAAAAAAAAZwYXBlcnMAEAAIAADQiZxKAAAAEQAIAADRCkIKAAAAAQAQABEllgAJYWMACTsdAAKT0wACAFdNYWNpbnRvc2ggSEQ6VXNlcnM6AHRpbGxob2ZmbWFubjoAZ29vZ2xlX2RyaXZlOgBwYXBlcnM6AENoZW4yMDAwIC0gUHJvYmFiaWxpIzI4NkE2NS5wZGYAAA4AlgBKAEMAaABlAG4AMgAwADAAMAAgAC0AIABQAHIAbwBiAGEAYgBpAGwAaQB0AHkAIABEAGUAbgBzAGkAdAB5ACAARgB1AG4AYwB0AGkAbwBuACAARQBzAHQAaQBtAGEAdABpAG8AbgAgAFUAcwBpAG4AZwAgAEcAYQBtAG0AYQAgAEsAZQByAG4AZQBsAHMALgBwAGQAZgAPABoADABNAGEAYwBpAG4AdABvAHMAaAAgAEgARAASAHFVc2Vycy90aWxsaG9mZm1hbm4vZ29vZ2xlX2RyaXZlL3BhcGVycy9DaGVuMjAwMCAtIFByb2JhYmlsaXR5IERlbnNpdHkgRnVuY3Rpb24gRXN0aW1hdGlvbiBVc2luZyBHYW1tYSBLZXJuZWxzLnBkZgAAEwABLwAAFQACABP//wAAgAbSGxwdHlokY2xhc3NuYW1lWCRjbGFzc2VzXU5TTXV0YWJsZURhdGGjHR8gVk5TRGF0YVhOU09iamVjdNIbHCIjXE5TRGljdGlvbmFyeaIiIF8QD05TS2V5ZWRBcmNoaXZlctEmJ1Ryb290gAEACAARABoAIwAtADIANwBAAEYATQBVAGAAZwBqAGwAbgBxAHMAdQB3AIQAjgD4AP0BBQNvA3EDdgOBA4oDmAOcA6MDrAOxA74DwQPTA9YD2wAAAAAAAAIBAAAAAAAAACgAAAAAAAAAAAAAAAAAAAPd},
	Bdsk-Url-1 = {http://dx.doi.org/10.1023/A%3A1004165218295},
	Bdsk-Url-2 = {http://dx.doi.org/10.1023/A:1004165218295}}

@article{Jones1994,
	Author = {Jones, M.C. and McKay, I.J. and Hu, T.-C.},
	Date-Added = {2015-12-16 18:47:45 +0000},
	Date-Modified = {2016-01-05 14:49:37 +0000},
	Doi = {10.1007/BF00773515},
	Journal = {Ann. Inst. Statist. Math.},
	Keywords = {kernel, Density estimation},
	Number = {3},
	Pages = {521--535},
	Title = {Variable location and scale kernel density estimation},
	Volume = {46},
	Year = {1994},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/BF00773515}}

@article{Hall2002a,
	Abstract = {It is shown that data sharpening can be used to produce density estimators that enjoy arbitrarily high orders of bias reduction. Practical advantages of this approach, relative to competing methods, are demonstrated. They include the sheer simplicity of the estimators, which makes code for computing them particularly easy to write, very good mean-squared error performance, reduced `wiggliness' of estimates and greater robustness against undersmoothing.},
	Author = {Hall, Peter and Minnotte, Michael C.},
	Date-Added = {2015-12-16 18:47:28 +0000},
	Date-Modified = {2016-01-05 14:47:51 +0000},
	Doi = {10.1111/1467-9868.00329},
	Journal = {J. R. Stat. Soc. Ser. B. Stat. Methodol.},
	Keywords = {Density estimation; sharpening},
	Number = {1},
	Pages = {141--157},
	Title = {High order data sharpening for density estimation},
	Volume = {64},
	Year = {2002},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1111/1467-9868.00329}}

@article{Samuiddin1990,
	Abstract = {The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is the only admissible kernel. An analysis of kernel density estimates leads to two new methods of bias reduction. We also discuss a general method of improving kernel density estimates in the sense of having smaller mean squared error. Finally we provide a theoretical framework for the operational method of using Abramson's square root law providing tangible and compact forms for the bias and mean squared error.},
	Author = {Samuiddin, M. and El-Sayyad, G. M.},
	Date-Added = {2015-12-16 18:47:19 +0000},
	Date-Modified = {2016-01-05 14:51:33 +0000},
	Doi = {10.1093/biomet/77.4.865},
	Journal = {Biometrika},
	Number = {4},
	Pages = {865--874},
	Title = {On nonparametric kernel density estimates},
	Volume = {77},
	Year = {1990},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://biomet.oxfordjournals.org/content/77/4/865.abstract},
	Bdsk-Url-2 = {http://dx.doi.org/10.1093/biomet/77.4.865}}

@article{Terrell1992,
	Author = {Terrell, George R. and Scott, David W.},
	Date-Added = {2015-12-16 18:47:11 +0000},
	Date-Modified = {2015-12-16 18:47:11 +0000},
	Doi = {10.1214/aos/1176348768},
	Journal = {Ann. Statist.},
	Keywords = {Density estimation},
	Month = {09},
	Number = {3},
	Pages = {1236--1265},
	Title = {Variable Kernel Density Estimation},
	Volume = {20},
	Year = {1992},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1214/aos/1176348768}}

@book{Silverman1986,
	Author = {Bernard W. Silverman},
	Date-Added = {2015-12-16 18:46:55 +0000},
	Date-Modified = {2015-12-16 18:46:55 +0000},
	Keywords = {Density estimation; transformation; renormalisation; orthogonal series; reflection; kernel},
	Publisher = {Chapman and Hall},
	Title = {Density Estimation for Statistics and Data Analysis},
	Year = {1986},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://www.amazon.com/Density-Estimation-Statistics-Data-Analysis/dp/0412246201%3FSubscriptionId%3D0JYN1NVW651KCA56C102%26tag%3Dtechkie-20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0412246201}}

gha_language: TeX


text:
@misc{Grinstein:2002:IVV,
  author = {Georges Grinstein and Daniel Keim and Matthew Ward},
  title = {Information Visualization, Visual Data Mining, and Its Application to Drug Design},
  howpublished = {IEEE Visualization Course \#1 Notes},
  month = {October},
  year = {2002},
	url = {http://vis.computer.org/vis2002/program/tutorials/tutorial_01_abstract.html}
}

@Article{Isenberg:2017:VMC,
  author = {Petra Isenberg and Florian Heimerl and Steffen Koch and Tobias Isenberg and Panpan Xu and Chad Stolper and Michael Sedlmair and Jian Chen and Torsten M{\"o}ller and John Stasko},
	title = {{vispubdata.org: A Metadata Collection about {IEEE} Visualization ({VIS}) Publications}},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	year = {2017},
	volume = {23},
	OPTnumber = {},
	OPTmonth = {},
	OPTpages = {},
	doi = {http://dx.doi.org/10.1109/TVCG.2016.2615308},
  OPTgooglescholarid = {},
	note = {To appear},
}

@mastersthesis{Kindlmann:1999:SAG,
  author = {Gordon Kindlmann},
  title = {Semi-Automatic Generation of Transfer Functions for Direct Volume Rendering},
  school = {Cornell University},
	address = {USA},
	url = {http://www.graphics.cornell.edu/pubs/1999/Kin99.html},
  year = 1999,
}

@manual{Kitware:2003,
  title = {The Visualization Toolkit User's Guide},
  organization = {Kitware, Inc.},
  year = {2003},
  month = {January},
	url = {http://www.kitware.com/publications/item/view/1269}
}

@phdthesis{Levoy:1989:DSV,
  author = {Marc Levoy},
  title = {Display of Surfaces from Volume Data},
  school = {University of North Carolina at Chapel Hill},
	address = {USA},
	url = {http://www.cs.unc.edu/techreports/89-022.pdf},
  year = {1989},
}

@article{Hipper,
 author = {Hajo Hipper, René Rentzmann},
 title = {Text Mining},
 journal = {Informatik Spektrum},
 year = {2006}
}

@book{Weiss,
  author = {Sholom M.Weiss},
  title = {FundamentalsofPredicitiveTextMining},
  year = 2010,
  publisher = {Springer-Verlag London Limited},
  address = {London}
}

@article{Max:1995:OMF,
 author = {Max, Nelson},
 title = {Optical Models for Direct Volume Rendering},
 journal = {IEEE Transactions on Visualization and Computer Graphics},
 volume = {1},
 number = {2},
 month = jun,
 year = {1995},
 pages = {99--108},
 doi = {10.1109/2945.468400},
}

@inproceedings{Nielson:1991:TAD,
  author = {Gregory M. Nielson and Bernd Hamann},
  title = {The Asymptotic Decider: Removing the Ambiguity in Marching Cubes},
  year = {1991},
  booktitle = {Proc.\ Visualization},
  pages = {83--91},
  doi={10.1109/VISUAL.1991.175782},
	publisher = {IEEE Computer Society},
	address = {Los Alamitos},
}

@book{Ware:2004:IVP,
  author = {Colin Ware},
  title = {Information Visualization: Perception for Design},
  edition = {2\textsuperscript{nd}},
  year = 2004,
  publisher = {Morgan Kaufmann Publishers Inc.},
  address = {San Francisco},
	doi = {https://dx.doi.org/10.1016/B978-155860819-1/50001-7}
}

@ARTICLE{Wyvill:1986:DSS,
  author = {Wyvill, Geoff and McPheeters, Craig and Wyvill, Brian},
  title = {Data Structure for \emph{soft} Objects},
  journal = {The Visual Computer},
  year = {1986},
  volume = {2},
  pages = {227--234},
  number = {4},
  month = aug,
  doi = {http://dx.doi.org/10.1007/BF01900346}
}

repository: slemke/text-visualizer

repo_name: slemke/text-visualizer

gha_language: JavaScript


text:
import Vue from 'vue'
import App from './App.vue'
import router from './router'
import 'lib-flexible/flexible.js'
import 'mint-ui/lib/style.css'
Vue.config.productionTip = false
//5: 设置请求的根路径
//Vue.http.options.root = "http://127.0.0.1/vue_ser/";
//6:全局设置post 时候表音的数据组织格式为 application/x-www-form-urlencoded
//Vue.http.options.emulateJSON = true;
// 导入 MUI 的样式表， 和 Bootstrap 用法没有差别
import './lib/mui/css/mui.css'
// 导入 MUI 的样式表，扩展图标样式，购物车图标
// 还需要加载图标字体文件
import './lib/mui/css/icons-extra.css'

//1:引入mint-ui Header组件
import {Header,Swipe,SwipeItem,Button} from "mint-ui"
//2:注册Header组件
Vue.component(Header.name,Header);
//3:注册Swipe/SwipeItem组件
Vue.component(Swipe.name,Swipe);
Vue.component(SwipeItem.name,SwipeItem);
Vue.component(Button.name,Button);
//4.1:引入Vuex
import Vuex from "vuex"
//4.2:注册Vuex组件
Vue.use(Vuex)
//4.3:创建Vuex实例对象
var store = new Vuex.Store({
  state:{
    cartCount:0   //购物车中商品数量
  },
  mutations:{     // 修改共享数据方法
    increment(state,count){
       state.cartCount+=count
    },
    substract(state){ state.cartCount--},
    clear(state){state.cartCount = 0}
  },
  getters:{      //获取共享数据方法
     optCartCount:function(state){
       return state.cartCount;
     }
  }
})
//5.1:引入axios库(VueResource)
import axios from "axios"
//5.2:跨域保存session值
axios.defaults.withCredentials=true
//5.3:注册组件
Vue.prototype.axios=axios
//4:引入vue-resource 库
import VueResource from "vue-resource";
//5:注册vue-resource 库
Vue.use(VueResource)
//6:配置vue-resource常见属性
//6.1:设置请求服务器根目录
Vue.http.options.root = "http://127.0.0.1:3000/";
//6.2:全局设置post 时候表单 //application/x-www-form-urlencoded
//将提交数据进行转码操作
Vue.http.options.emulateJSON = true
//6.3:跨域访问保存session值选项
Vue.http.options.withCredentials = true
//7. 创建日期类型过滤器
//val：原先日期对象
Vue.filter("datatimeFilter",function(val){
  //7.1:创建日期对象
  var data = new Date(val)
  //7.2:获取年月日时分秒
  var y = data.getFullYear();
  var m = data.getMonth()+1;
  var d = data.getDate();
  var h = data.getHours();
  var mi = data.getMinutes();
  var s = data.getSeconds();
  //7.3:月日格式判断 07 08 09 10
  m<10&&(m="0"+m);
  d<10&&(d="0"+d);
  //7.4:拼接字符串返回
  return `${y}-${m}-${d} ${h}:${mi}:${s}`;
})
new Vue({
  router,
  render: h => h(App),
  store       //将Vuex对象注册 Vue实例
}).$mount('#app')

extension: js