Journal of Statistical Software
最新影响因子 - 实时趋势预测 & 排名分区分析





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Highly Cited Articles

Journal of Statistical Software

High Impact Research Articles
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Highly Cited Keywords

Journal of Statistical Software

High Impact Research Keywords


Journal of Statistical Software

Journal of Statistical Software 2021-2022 年的影响因子为6.44。

Journal of Statistical Software Impact Factor
最高影响因子 IF

近十年Journal of Statistical Software的最高影响因子为22.737。

最低影响因子 IF

近十年Journal of Statistical Software的最低影响因子为2.379。

影响因子 总成长率
影响因子 总成长率

近十年Journal of Statistical Software的影响因子总成长率为60.6%。

影响因子 平均成长率
影响因子 平均成长率

近十年Journal of Statistical Software的影响因子平均成长率为6.1%。


子领域 分区 排名 百分比
Statistics and Probability 1区 1/239

Statistics and Probability 99%

Statistics, Probability and Uncertainty 1区 1/152

Statistics, Probability and Uncertainty 99%

Software 1区 10/389

Software 97%


· 在Statistics and Probability研究领域,Journal of Statistical Software的分区数为1区。Journal of Statistical Software在Statistics and Probability研究类别的239种相关期刊中排名第1。在Statistics and Probability领域,Journal of Statistical Software的排名百分位约为99%。
· 在Statistics, Probability and Uncertainty研究领域,Journal of Statistical Software的分区数为1区。Journal of Statistical Software在Statistics, Probability and Uncertainty研究类别的152种相关期刊中排名第1。在Statistics, Probability and Uncertainty领域,Journal of Statistical Software的排名百分位约为99%。
· 在Software研究领域,Journal of Statistical Software的分区数为1区。Journal of Statistical Software在Software研究类别的389种相关期刊中排名第10。在Software领域,Journal of Statistical Software的排名百分位约为97%。

Journal of Statistical Software Impact Factor 2022-2023 Prediction

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2020-2021 6.44
2019-2020 13.642
2018-2019 11.655
2017-2018 22.737
2016-2017 9.436
2015-2016 2.379
2014-2015 3.801
2013-2014 3.801
2012-2013 4.91
2011-2012 4.01

· Journal of Statistical Software 2020-2021年的影响因子为6.44
· Journal of Statistical Software 2019-2020年的影响因子为13.642
· Journal of Statistical Software 2018-2019年的影响因子为11.655
· Journal of Statistical Software 2017-2018年的影响因子为22.737
· Journal of Statistical Software 2016-2017年的影响因子为9.436
· Journal of Statistical Software 2015-2016年的影响因子为2.379
· Journal of Statistical Software 2014-2015年的影响因子为3.801
· Journal of Statistical Software 2013-2014年的影响因子为3.801
· Journal of Statistical Software 2012-2013年的影响因子为4.91
· Journal of Statistical Software 2011-2012年的影响因子为4.01


出版数量 引用数量
出版数量 引用数量
1996 3 0
1997 10 2
1998 2 7
1999 9 4
2000 9 32
2001 7 45
2002 13 74
2003 21 201
2004 39 321
2005 45 494
2006 38 693
2007 83 956
2008 64 1421
2009 72 2105
2010 87 3405
2011 105 5087
2012 93 7222
2013 61 9725
2014 90 13154
2015 105 16274
2016 102 21061
2017 107 27957
2018 88 32313
2019 56 41950
2020 76 57412
2021 4 4761

· Journal of Statistical Software于1996年发表了3篇报告,并取得0篇引用文献。
· Journal of Statistical Software于1997年发表了10篇报告,并取得2篇引用文献。
· Journal of Statistical Software于1998年发表了2篇报告,并取得7篇引用文献。
· Journal of Statistical Software于1999年发表了9篇报告,并取得4篇引用文献。
· Journal of Statistical Software于2000年发表了9篇报告,并取得32篇引用文献。
· Journal of Statistical Software于2001年发表了7篇报告,并取得45篇引用文献。
· Journal of Statistical Software于2002年发表了13篇报告,并取得74篇引用文献。
· Journal of Statistical Software于2003年发表了21篇报告,并取得201篇引用文献。
· Journal of Statistical Software于2004年发表了39篇报告,并取得321篇引用文献。
· Journal of Statistical Software于2005年发表了45篇报告,并取得494篇引用文献。
· Journal of Statistical Software于2006年发表了38篇报告,并取得693篇引用文献。
· Journal of Statistical Software于2007年发表了83篇报告,并取得956篇引用文献。
· Journal of Statistical Software于2008年发表了64篇报告,并取得1421篇引用文献。
· Journal of Statistical Software于2009年发表了72篇报告,并取得2105篇引用文献。
· Journal of Statistical Software于2010年发表了87篇报告,并取得3405篇引用文献。
· Journal of Statistical Software于2011年发表了105篇报告,并取得5087篇引用文献。
· Journal of Statistical Software于2012年发表了93篇报告,并取得7222篇引用文献。
· Journal of Statistical Software于2013年发表了61篇报告,并取得9725篇引用文献。
· Journal of Statistical Software于2014年发表了90篇报告,并取得13154篇引用文献。
· Journal of Statistical Software于2015年发表了105篇报告,并取得16274篇引用文献。
· Journal of Statistical Software于2016年发表了102篇报告,并取得21061篇引用文献。
· Journal of Statistical Software于2017年发表了107篇报告,并取得27957篇引用文献。
· Journal of Statistical Software于2018年发表了88篇报告,并取得32313篇引用文献。
· Journal of Statistical Software于2019年发表了56篇报告,并取得41950篇引用文献。
· Journal of Statistical Software于2020年发表了76篇报告,并取得57412篇引用文献。
· Journal of Statistical Software于2021年发表了4篇报告,并取得4761篇引用文献。
· Journal of Statistical Software的总出版物为1389。
· Journal of Statistical Software的总引用为246676。

Journal of Statistical Software
Journal of Statistical Software | Academic Accelerator - About the Journal


The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics. The Journal of Statistical Software is a peer-reviewed open-access scientific journal that publishes papers related to statistical software. The Journal of Statistical Software was founded in 1996 by Jan de Leeuw of the Department of Statistics at the University of California, Los Angeles. Its current editors-in-chief are Achim Zeileis, Bettina Grün, Edzer Pebesma, and Torsten Hothorn. It is published by the Foundation for Open Access Statistics. The journal charges no author fees or subscription fees.


Journal of Statistical Software的ISSN是 1548-7660 ISSN是一个8位数的代码,用于识别各种报纸,期刊,杂志和期刊以及所有媒体 - 包括印刷版和电子版。

ISSN (Online)
ISSN (Online)

Journal of Statistical Software的ISSN(Online)是 - . ISSN是一个8位数的代码,用于识别各种报纸,期刊,杂志和期刊以及所有媒体 - 包括印刷版和电子版。

University of California at Los Angeles

Journal of Statistical Software的出版社是 University of California at Los Angeles


Journal of Statistical Software publishes reports - .

1996 - Present

Journal of Statistical Software的出版年度包含 1996 - Present .




The language of Journal of Statistical Software is English .

United States

The publisher of Journal of Statistical Software is University of California at Los Angeles , which locates in United States .

What is Impact Factor?

The impact factor (IF) or journal impact factor (JIF) of an academic journal is a scientometric index calculated by Clarivate that reflects the yearly average number of citations of articles published in the last two years in a given journal. It is frequently used as a proxy for the relative importance of a journal within its field; journals with higher impact factor values are often deemed to be more important, or carry more intrinsic prestige in their respective fields, than those with lower values.

Journal of Statistical Software | Academic Accelerator - About the Impact Factor

Impact factor is commonly used to evaluate the relative importance of a journal within its field and to measure the frequency with which the “average article” in a journal has been cited in a particular time period. Journal which publishes more review articles will get highest IFs. Journals with higher IFs believed to be more important than those with lower ones. According to Eugene Garfield “impact simply reflects the ability of the journals and editors to attract the best paper available.” Journal which publishes more review articles will get maximum IFs. The Impact Factor of an academic journal is a scientometric Metric that reflects the yearly average number of citations that recent articles published in a given journal received. It is frequently used as a Metric for the relative importance of a journal within its field; journals with higher Impact Factor are often deemed to be more important than those with lower ones. The Journal of Statistical Software Impact Factor IF measures the average number of citations received in a particular year (2021) by papers published in the Journal of Statistical Software during the two preceding years (2019-2020). Note that 2021 Impact Factor are reported in 2022; they cannot be calculated until all of the 2021 publications have been processed by the indexing agency. New journals, which are indexed from their first published issue, will receive an impact factor after two years of indexing; in this case, the citations to the year prior to Volume 1, and the number of articles published in the year prior to Volume 1, are known zero values. Journals that are indexed starting with a volume other than the first volume will not get an impact factor until they have been indexed for three years. Occasionally, Journal Citation Reports assigns an impact factor to new journals with less than two years of indexing, based on partial citation data. The calculation always uses two complete and known years of item counts, but for new titles one of the known counts is zero. Annuals and other irregular publications sometimes publish no items in a particular year, affecting the count. The impact factor relates to a specific time period; it is possible to calculate it for any desired period. In addition to the 2-year Impact Factor, the 3-year Impact Factor, 4-year Impact Factor, 5-year Impact Factor, Real-Time Impact Factor can provide further insights and factors into the impact of Journal of Statistical Software.


The impact factor was devised by Eugene Garfield, the founder of the Institute for Scientific Information (ISI). Impact factors are calculated yearly starting from 1975 for journals listed in the Journal Citation Reports (JCR). ISI was acquired by Thomson Scientific & Healthcare in 1992, and became known as Thomson ISI. In 2018, Thomson ISI was sold to Onex Corporation and Baring Private Equity Asia. They founded a new corporation, Clarivate, which is now the publisher of the JCR.


The impact factor is used to compare different journals within a certain field. The Web of Science indexes more than 11,500 science and social science journals. Journal impact factors are often used to evaluate the merit of individual articles and individual researchers. This use of impact factors was summarised by Hoeffel:

Impact Factor is not a perfect tool to measure the quality of articles but there is nothing better and it has the advantage of already being in existence and is, therefore, a good technique for scientific evaluation. Experience has shown that in each specialty the best journals are those in which it is most difficult to have an article accepted, and these are the journals that have a high impact factor. Most of these journals existed long before the impact factor was devised. The use of impact factor as a measure of quality is widespread because it fits well with the opinion we have in each field of the best journals in our specialty....In conclusion, prestigious journals publish papers of high level. Therefore, their impact factor is high, and not the contrary.

Eugene Garfield

In brief, Impact factors may be used by:
  • Authors to decide where to submit an article for publication.
  • Libraries to make collection development decisions
  • Academic departments to assess academic productivity
  • Academic departments to make decisions on promotion and tenure.
As impact factors are a journal-level metric, rather than an article- or individual-level metric, this use is controversial. Garfield agrees with Hoeffel,but warns about the "misuse in evaluating individuals" because there is "a wide variation [of citations] from article to article within a single journal". Other things to consider about Impact Factors:
  • Many journals do not have an impact factor.
  • The impact factor cannot assess the quality of individual articles. Even if citations were evenly distributed among articles, the impact factor would only measure the interests of other researchers in an article, not its importance and usefulness.
  • Only research articles, technical notes and reviews are “citable” items. Editorials, letters, news items and meeting abstracts are “non-citable items”.
  • Only a small percentage of articles are highly cited and they are found in a small subset of journals. This small proportion accounts for a large percentage of citations.
  • Controversial papers, such as those based on fraudulent data, may be highly cited, distorting the impact factor of a journal.
  • Citation bias may exist. For example, English language resources may be favoured. Authors may cite their own work.
Moreover, informed and careful use of these impact data is essential, and should be based on a thorough understanding of the methodology used to generate impact factors. There are controversial aspects of using impact factors:
  • It is not clear whether the number of times a paper is cited measures its actual quality.
  • Some databases that calculate impact factors fail to incorporate publications including textbooks, handbooks and reference books.
  • Certain disciplines have low numbers of journals and usage. Therefore, one should only compare journals or researchers within the same discipline.
  • Review articles normally are cited more often and therefore can skew results.
  • Self-citing may also skew results.
  • Some resources used to calculate impact factors have inadequate international coverage.
  • Editorial policies can artificially inflate an impact factor.
Impact factors have often been used in advancement and tenure decision-making. Many recognize that this is a coarse tool for such important decisions, and that a multitude of factors should be taken into account in these deliberations. When considering the use of the impact factor (IF), keep these aspects in mind:
  • IF analysis is limited to citations from the journals indexed by the Web of Science/Web of Knowledge. Currently, the Web of Science indexes only 8621 journals across the full breadth of the sciences, and just 3121 in the social sciences.
  • A high IF/citation rate says nothing about the quality -- or even, validity -- of the references being cited. Notorious or even retracted articles often attract a lot of attention, hence a high number of citations. The notority related to the first publication on "cold fusion" is one such example.
  • Journals that publish more "review articles" are often found near the top of the rankings. While not known for publishing new, creative findings, these individual articles tend to be heavily cited.
  • The IF measures the average number of citations to articles in the journal -- given this, a small number of highly-cited articles will skew the figure.
  • It takes several years for new journals to be added to the list of titles indexed by the Web of Science/Web of Knowledge, so these newer titles will be under-represented.
  • It's alleged that journal editors have learned to "game" the system, encouraging authors to cite their works previously published in the same journal.
Comparing Journals Across Disciplines? Not a good idea! Using Impact Factors within a given discipline should only be done with great care, as described above. Using impact factor data to compare journals across disciplines is even more problematic. Here are some of the reasons:
  • Disciplines where older literature is still referenced, such as Chemistry and Mathematics, offer challenges to the methodolgy since older citations (older than two years) are not used to calculate the impact factor for a given journal. (Five-year impact factor analysis, which can be calculated using the Journal Citation Index database, helps smooth out this problem only to some degree.)
  • Different disciplines have different practices regarding tendency to cite larger numbers of references. Higher overall citation rates will bump upward impact factor measurements.
  • Where it's common for large numbers of authors to collaborate on a single paper, such as in Physics, the tendency of authors to cite themselves (and in this case, more authors) will result in increased citation rates.

Pros and Cons of the Impact Factor


  • A vetted, established metric for measuring journal impact within a discipline.
  • Designed to eliminate bias based on journal size and frequency.
  • Individual articles makes an uneven contribution to overall Impact Factor.
  • Impact Factor does not account for certain things, things like context (postive or negative citaion) and intentionality (self-citation).
  • The metric is proprietary to and bound by the contents of the Thomson Reuters database.
  • Citations, on which the Impact Factor is based, count for less than 1% of an article's overall use.


Numerous critiques have been made regarding the use of impact factors. A 2007 study noted that the most fundamental flaw is that impact factors present the mean of data that are not normally distributed, and suggested that it would be more appropriate to present the median of these data. There is also a more general debate on the validity of the impact factor as a measure of journal importance and the effect of policies that editors may adopt to boost their impact factor (perhaps to the detriment of readers and writers). Other criticism focuses on the effect of the impact factor on behavior of scholars, editors and other stakeholders. Others have made more general criticisms, arguing that emphasis on impact factor results from negative influence of neoliberal policies on academia claiming that what is needed is not just replacement of the impact factor with more sophisticated metrics for science publications but also discussion on the social value of research assessment and the growing precariousness of scientific careers in higher education.
Experts stress that there are limitations in using impact factors to evaluate a scholar's work. There are many reasons cited for not relying on impact factor alone to evaluate the output of a particular individual. Among these are the following:

  • A single factor is not sufficient for evaluating an author's work.
  • Journal values are meaningless unless compared within the same discipline. Impact factors vary among disciplines.
  • The impact factor was originally devised to show the impact of a specific journal, not a specific scholar. The quality and impact of the author's work may extend beyond the impact of a particular journal.
According to Jim Testa, a researcher for ThomsonReuters Scientific, the most widespread misuse of the Impact Factor is to evaluate the work of an individual author (instead of a journal). "To say that because a researcher is publishing in a certain journal, he or she is more influential or deserves more credit is not necessarily true. There are many other variables to consider." (interview 6/26/2008 in Thomson Reuters blog entry)


影响因子(IF)经常用作表明期刊对其领域重要性的指标。它是由科学信息研究所的创始人Eugene Garfield首次提出的。尽管IF被机构和临床医生广泛使用,但是人们对于IF日记的计算方法,其意义以及如何利用它存在着广泛的误解。期刊的影响因子与同行评议过程的质量和期刊的内容质量等因素无关,而是一种反映对期刊,书籍,论文,项目报告,报纸上发表的文章的平均引用次数的度量,会议/研讨会论文集,在互联网上发布的文件,说明以及任何其他批准的文件。

Journal of Statistical Software | Academic Accelerator - About the Impact Factor

影响因子通常用于评估期刊在其领域内的相对重要性,以及衡量期刊在特定时间段内引用“平均文章”的频率。发表更多评论文章的期刊将获得最高的IF。 IF较高的期刊被认为比IF较低的期刊更重要。根据尤金·加菲尔德(Eugene Garfield)的说法,“影响只是反映期刊和编辑吸引最佳论文的能力。”发表更多评论文章的期刊将获得最大的IF。

Scientific Writng Keywords