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R Packages Maintainer





In [1]:



    


import pandas
import re

from matplotlib import pyplot as plt

%matplotlib inline
from IPython.display import set_matplotlib_formats
#set_matplotlib_formats('pdf')

data = pandas.DataFrame.from_csv('../data/github-cran-150601.csv')
data['Date'] = pandas.to_datetime(data['Date'])



    











In [2]:



    


print len(data.drop_duplicates('Maintainer')), 'maintainer values'



    


















    






8533 maintainer values

















In [3]:



    


def clean_maintainer(v):
    v = v if isinstance(v, str) else ''
    words = map(str.strip, v.split(' '))
    words = map(lambda w: re.sub(r'[^@a-zA-Z]', '', w), words)
    words = filter(lambda w: len(w) > 2 and '@' not in w, words)
    words.sort()
    return ' '.join(words)

data['Maintainer_can'] = data['Maintainer'].apply(clean_maintainer)
print len(data.drop_duplicates('Maintainer_can')), 'canonical maintainer values'



    


















    






6044 canonical maintainer values

















In [4]:



    


_maint_first = data.sort('Date').drop_duplicates('Maintainer_can').set_index('Maintainer_can')[['Date', 'InGitHub', 'InCRAN']]
_maint_last = data.sort('Date', ascending=False).drop_duplicates('Maintainer_can').set_index('Maintainer_can')[['Date', 'InGitHub', 'InCRAN']]

github = data.query('InGitHub == 1').sort('Date')
cran = data.query('InCRAN == 1').sort('Date')

__f = (lambda d,rev,name: d.drop_duplicates('Maintainer_can', take_last=rev)
               .set_index('Maintainer_can')[['Date']]
               .rename(columns={'Date':name}))

maintainers = __f(github, False, 'GitHubFirstDate').join(
    [__f(github, True, 'GitHubLastDate'), __f(cran, False, 'CRANFirstDate'), __f(cran, True, 'CRANLastDate'),],
    how='outer')



    











In [5]:



    


maintainers['DaysOnGitHub'] = maintainers['GitHubLastDate'] - maintainers['GitHubFirstDate']
maintainers['DaysOnCRAN'] = maintainers['CRANLastDate'] - maintainers['CRANFirstDate']
# We consider CRAN first
maintainers['CRANFirst-->GHFirst'] = maintainers['GitHubFirstDate'] - maintainers['CRANFirstDate'] 
maintainers['CRANLast-->GitHubLast'] = maintainers['GitHubLastDate'] - maintainers['CRANLastDate']
maintainers['CRANLast-->GitHubFirst'] = maintainers['GitHubFirstDate'] - maintainers['CRANLastDate']
maintainers['CRANFirst-->GitHubLast'] = maintainers['GitHubLastDate'] - maintainers['CRANFirstDate']

fields = ['DaysOnGitHub', 'DaysOnCRAN', 'CRANFirst-->GHFirst', 'CRANLast-->GitHubLast', 'CRANLast-->GitHubFirst', 'CRANFirst-->GitHubLast']
for field in fields:
    maintainers[field] = maintainers[field].astype('timedelta64[D]')



    











In [6]:



    


from collections import OrderedDict

testdata = OrderedDict([
    ('all', maintainers), 
    ('on CRAN', maintainers.dropna(subset=['CRANFirstDate', 'CRANLastDate'])),
    ('on GH', maintainers.dropna(subset=['GitHubFirstDate', 'GitHubLastDate'])),
    ('on both', maintainers.dropna()),
    ('on both at least 1 minute', maintainers.dropna().query('GitHubFirstDate <= CRANLastDate and GitHubLastDate >= CRANFirstDate')),
    ('first on CRAN', maintainers.dropna().query('CRANFirstDate <= GitHubFirstDate')),
    ('first on GH', maintainers.dropna().query('GitHubFirstDate <= CRANFirstDate')),
    ('migrate from GH to CRAN', maintainers.dropna().query('GitHubLastDate <= CRANFirstDate')),
    ('migrate from CRAN to GH', maintainers.dropna().query('CRANLastDate <= GitHubFirstDate')),
])

for key, value in testdata.iteritems():
    print key
    print len(value), 'maintainers of', len(data[data['Maintainer_can'].isin(value.index)].drop_duplicates(subset=['Package'])), 'packages'
    for field in fields:
        print field, '| mean:', value[field].mean(), '| median:', value[field].median(), '| stddev:', value[field].std()
    print



    


















    






all
6044 maintainers of 11442 packages
DaysOnGitHub | mean: 275.196850394 | median: 82.0 | stddev: 452.322268348
DaysOnCRAN | mean: 817.769325658 | median: 380.5 | stddev: 1084.94367843
CRANFirst-->GHFirst | mean: 597.544827586 | median: 137.0 | stddev: 1073.92398917
CRANLast-->GitHubLast | mean: 103.053793103 | median: 0.0 | stddev: 396.265029521
CRANLast-->GitHubFirst | mean: -355.652413793 | median: -259.0 | stddev: 707.699850157
CRANFirst-->GitHubLast | mean: 1056.27310345 | median: 706.0 | stddev: 1169.92601169

on CRAN
4864 maintainers of 10125 packages
DaysOnGitHub | mean: 458.275862069 | median: 276.0 | stddev: 581.670782258
DaysOnCRAN | mean: 817.769325658 | median: 380.5 | stddev: 1084.94367843
CRANFirst-->GHFirst | mean: 597.544827586 | median: 137.0 | stddev: 1073.92398917
CRANLast-->GitHubLast | mean: 103.053793103 | median: 0.0 | stddev: 396.265029521
CRANLast-->GitHubFirst | mean: -355.652413793 | median: -259.0 | stddev: 707.699850157
CRANFirst-->GitHubLast | mean: 1056.27310345 | median: 706.0 | stddev: 1169.92601169

on GH
1905 maintainers of 5841 packages
DaysOnGitHub | mean: 275.196850394 | median: 82.0 | stddev: 452.322268348
DaysOnCRAN | mean: 952.773793103 | median: 544.0 | stddev: 1140.48640691
CRANFirst-->GHFirst | mean: 597.544827586 | median: 137.0 | stddev: 1073.92398917
CRANLast-->GitHubLast | mean: 103.053793103 | median: 0.0 | stddev: 396.265029521
CRANLast-->GitHubFirst | mean: -355.652413793 | median: -259.0 | stddev: 707.699850157
CRANFirst-->GitHubLast | mean: 1056.27310345 | median: 706.0 | stddev: 1169.92601169

on both
725 maintainers of 4516 packages
DaysOnGitHub | mean: 458.275862069 | median: 276.0 | stddev: 581.670782258
DaysOnCRAN | mean: 952.773793103 | median: 544.0 | stddev: 1140.48640691
CRANFirst-->GHFirst | mean: 597.544827586 | median: 137.0 | stddev: 1073.92398917
CRANLast-->GitHubLast | mean: 103.053793103 | median: 0.0 | stddev: 396.265029521
CRANLast-->GitHubFirst | mean: -355.652413793 | median: -259.0 | stddev: 707.699850157
CRANFirst-->GitHubLast | mean: 1056.27310345 | median: 706.0 | stddev: 1169.92601169

on both at least 1 minute
509 maintainers of 4096 packages
DaysOnGitHub | mean: 613.404715128 | median: 424.0 | stddev: 623.723131321
DaysOnCRAN | mean: 1113.14734774 | median: 767.0 | stddev: 1165.63265403
CRANFirst-->GHFirst | mean: 506.611001965 | median: 41.0 | stddev: 1000.71954524
CRANLast-->GitHubLast | mean: 6.84872298625 | median: -1.0 | stddev: 257.214253011
CRANLast-->GitHubFirst | mean: -607.029469548 | median: -411.0 | stddev: 612.973616595
CRANFirst-->GitHubLast | mean: 1120.5108055 | median: 755.0 | stddev: 1159.68419302

first on CRAN
434 maintainers of 3476 packages
DaysOnGitHub | mean: 421.3640553 | median: 163.0 | stddev: 607.382112949
DaysOnCRAN | mean: 1352.62442396 | median: 1038.0 | stddev: 1275.67457478
CRANFirst-->GHFirst | mean: 1107.65207373 | median: 724.0 | stddev: 1117.14711553
CRANLast-->GitHubLast | mean: 176.313364055 | median: 37.5 | stddev: 467.675306777
CRANLast-->GitHubFirst | mean: -245.444700461 | median: -123.0 | stddev: 790.964271291
CRANFirst-->GitHubLast | mean: 1529.41705069 | median: 1165.5 | stddev: 1251.47586546

first on GH
291 maintainers of 1342 packages
DaysOnGitHub | mean: 513.326460481 | median: 362.0 | stddev: 537.382724933
DaysOnCRAN | mean: 356.432989691 | median: 186.0 | stddev: 470.504444786
CRANFirst-->GHFirst | mean: -163.233676976 | median: -71.0 | stddev: 213.74945138
CRANLast-->GitHubLast | mean: -6.20618556701 | median: -1.0 | stddev: 213.168345558
CRANLast-->GitHubFirst | mean: -520.017182131 | median: -370.0 | stddev: 520.541001446
CRANFirst-->GitHubLast | mean: 350.621993127 | median: 208.0 | stddev: 493.88724952

migrate from GH to CRAN
66 maintainers of 99 packages
DaysOnGitHub | mean: 115.424242424 | median: 18.0 | stddev: 179.974960703
DaysOnCRAN | mean: 48.4393939394 | median: 0.0 | stddev: 166.130544479
CRANFirst-->GHFirst | mean: -166.439393939 | median: -67.5 | stddev: 215.792074339
CRANLast-->GitHubLast | mean: -99.1818181818 | median: -21.5 | stddev: 190.49109885
CRANLast-->GitHubFirst | mean: -214.96969697 | median: -92.5 | stddev: 260.016151551
CRANFirst-->GitHubLast | mean: -50.6060606061 | median: -5.5 | stddev: 113.405450797

migrate from CRAN to GH
150 maintainers of 411 packages
DaysOnGitHub | mean: 82.7266666667 | median: 7.5 | stddev: 165.134027187
DaysOnCRAN | mean: 806.48 | median: 302.5 | stddev: 1097.35017457
CRANFirst-->GHFirst | mean: 1242.26666667 | median: 725.5 | stddev: 1210.02277236
CRANLast-->GitHubLast | mean: 518.493333333 | median: 385.0 | stddev: 546.007113884
CRANLast-->GitHubFirst | mean: 435.453333333 | median: 248.5 | stddev: 518.071457157
CRANFirst-->GitHubLast | mean: 1325.32 | median: 902.5 | stddev: 1195.93651669


















In [7]:



    


import lifelines
import datetime 

survival = maintainers.copy()
kmf = lifelines.KaplanMeierFitter()

fill = pandas.to_datetime(datetime.date(2015, 6, 1))



    


















    






Using pure Python version of concordance index. 
You can speed this up 100x by compiling the Fortran code with:
>>> python setup.py build_ext --inplace

















In [8]:



    


ax = plt.subplot()

loc = survival.dropna(subset=('GitHubFirstDate',),)
T, E = lifelines.utils.datetimes_to_durations(loc['GitHubFirstDate'], loc['CRANFirstDate'], fill_date=fill)
kmf.fit(T, event_observed=E, label='GitHub --> CRAN').plot(ax=ax)

loc = survival.dropna(subset=('CRANFirstDate',),)
T, E = lifelines.utils.datetimes_to_durations(loc['CRANFirstDate'], loc['GitHubFirstDate'], fill_date=fill)
kmf.fit(T, event_observed=E, label='CRAN --> GitHub').plot(ax=ax)



    


















    






Warning: some values of start_times are before end_times
Warning: some values of start_times are before end_times










    
Out[8]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f5879e36050>










    
























In [9]:



    


ax = plt.subplot()

limit = pandas.to_datetime(datetime.date(2015,1,1)) #datetime.date.today() - datetime.timedelta(weeks=13)


loc = survival.dropna(subset=('GitHubFirstDate',),)

_F = lambda r: r if r < limit else ''

T, E = lifelines.utils.datetimes_to_durations(loc['GitHubFirstDate'], loc['GitHubLastDate'].apply(_F), fill_date=fill)
kmf.fit(T, event_observed=E, label='GitHub').plot(ax=ax)

loc = survival.dropna(subset=('CRANFirstDate',),)
T, E = lifelines.utils.datetimes_to_durations(loc['CRANFirstDate'], loc['CRANLastDate'].apply(_F), fill_date=fill)
kmf.fit(T, event_observed=E, label='CRAN').plot(ax=ax)



    


















    






Warning: some values of start_times are before end_times
Warning: some values of start_times are before end_times










    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f5879e46410>










    
























In [31]:



    


gh = github.dropna(subset=['Version']).sort('Date', ascending=True).drop_duplicates('Package', take_last=True).set_index('Package')
cr = cran.dropna(subset=['Version']).sort('Date', ascending=True).drop_duplicates('Package', take_last=True).set_index('Package')



_ = gh.join(cr, how='inner', lsuffix='gh', rsuffix='cr')[['Versiongh', 'Versioncr', 'Dependenciesgh', 'Dependenciescr', 'Maintainer_cangh', 'Maintainer_cancr']]

#pandas.set_option('display.max_rows', None)
_.query('Maintainer_cangh != Maintainer_cancr and Dependenciescr != Dependenciesgh and Versioncr != Versiongh')



    


















    
Out[31]:










  
    

      
      Versiongh
      Versioncr
      Dependenciesgh
      Dependenciescr
      Maintainer_cangh
      Maintainer_cancr
    

    

      Package
      
      
      
      
      
      
    

  
  
    

      genefu
      1.15.1
      1.0.9
      amap survcomp mclust biomaRt R
      survcomp amap mclust gplots R
      Benjamin HaibeKains Markus Schroeder
      Benjamin HaibeKains
    

    

      graph
      0.0.0.9000
      1.30.0
      sp R
      methods stats tools utils R methods
      
      Falcon Seth
    

    

      Rfun
      0.1
      1.0
      data.table R
      NaN
      Andrew Brooks
      Huidong Tian
    

    

      roxygen
      1.0
      0.1-3
      stringr tools brew digest
      digest gsubfn
      Hadley Wickham
      Danenberg Peter
    

    

      nnls
      1.0
      1.4
      NaN
      NaN
      Donachy Ljiljana Shaun Zigic and
      Katharine Mullen
    

    

      corpora
      1.0.0
      0.4-3
      jsonlite
      R
      Csardi Gabor
      Evert Stefan
    

    

      RTAQ
      0.3
      0.2
      xts timeDate
      R xts timeDate
      Cornelissen Jonathan
      Jonathan
    

    

      primer
      0.1
      1.0
      R
      deSolve lattice
      Mahr Tristan
      Hank Stevens
    

    

      concord
      0.1
      1.4-9
      dplyr lme4 lubridate Matrix R
      NaN
      
      Jim Lemon
    

    

      cat
      0.1
      0.0-6.5
      R
      NaN
      
      Fernando Tusell
    

    

      gtx
      0.0.10
      0.0.8
      beeswarm survival meta rmeta data.table
      R survival
      
      Johnson Toby
    

    

      LaplacesDemon
      15.03.19
      13.03.04
      parallel R
      R parallel
      LLC Statisticat
      Hall Martina
    

    

      BiGGR
      1.5.0
      1.8
      hypergraph R rsbml hyperdraw LIM stringr
      R LIM igraph Rgraphviz minet graph rsbml limSo...
      Anand Gavai Hannes Hettling
      Anand Gavai
    

    

      eventstudies
      1.2
      1.1
      R zoo xts boot testthat sandwich
      R zoo xts boot
      Anand Chirag
      Bahure Vikram
    

    

      pcurve
      0.1
      0.6-4
      ggplot2
      R mgcv vegan MASS stats
      Dablander Fabian
      Gavin Simpson
    

    

      rv
      0.1
      2.3.1
      NaN
      R stats utils grDevices graphics parallel
      Hadley Wickham
      Jouni Kerman
    

    

      lazy
      0.1
      1.2-15
      R
      NaN
      
      Lerman Liran
    

    

      pedigree
      0.1
      1.4
      R
      Matrix HaploSim reshape
      
      Albart Coster
    

    

      ClustOfVar
      1.1
      0.8
      R PCAmixdata
      NaN
      
      Chavent Marie
    

    

      assertthat
      0.1.0.99
      0.1
      tools
      NaN
      
      Hadley Wickham
    

    

      rfishbase
      2.0.0
      0.2-2
      dplyr lazyeval httr tidyr RCurl R
      XML RCurl R
      
      Boettiger Carl
    

    

      rbhl
      0.1.7.99
      0.1.0
      httr XML jsonlite plyr
      httr XML RJSONIO plyr
      
      Chamberlain Scott
    

    

      rebird
      0.1.5.99
      0.1.1
      jsonlite httr dplyr
      RJSONIO httr RCurl plyr
      
      Maia Rafael
    

    

      Jmisc
      0.1
      0.3.1
      R ROCR pROC
      NaN
      
      Chan Julian TszKin
    

    

      itertools
      0.0.0.9000
      0.1-3
      R
      parallel R iterators
      
      Steve Weston
    

    

      rgauges
      0.2.2.99
      0.2.0
      ggplot2 lubridate plyr httr jsonlite reshape2 ...
      ggplot2 lubridate plyr httr reshape2 gridExtra...
      
      Chamberlain Scott
    

    

      solr
      0.1.9.999
      0.1.4
      dplyr plyr httr XML jsonlite
      plyr httr XML assertthat rjson
      
      Chamberlain Scott
    

    

      quipu
      1.9.5
      1.9.0
      agricolae stringr pixmap shiny xtable R
      R stats agricolae stringr pixmap shiny xtable
      
      Reinhard Simon
    

    

      ngram
      1.0
      1.1
      NaN
      R methods
      Who complain
      Drew Schmidt
    

    

      SOD
      0.1
      1.0
      NaN
      methods Rcpp
      Noam Ross
      Jakt Martin
    

    

      translateR
      0.1
      1.0
      car data.table e1071 foreign plyr stringr tidy...
      RJSONIO RCurl textcat parallel httr
      Andreas Wygrabek
      Christopher Lucas
    

    

      refund.wave
      0.1-1
      0.1
      MASS glmnet wavethresh ncvreg R
      R glmnet wavethresh
      Huo Lan
      Adam Ciarleglio
    

    

      classify
      0.1
      1.3
      MASS bdsmatrix mvtnorm R
      Rcpp plyr ggplot2 lattice methods R2jags reshape2
      John Ramey
      Chris Wheadon
    

    

      rsunlight
      0.3.2.99
      0.3.0
      httr plyr jsonlite stringr
      httr plyr jsonlite stringr assertthat
      
      Chamberlain Scott
    

    

      agop
      0.2-0
      0.1-4
      R stats grDevices graphics
      R stats grDevices graphics Matrix igraph
      
      Gagolewski Marek
    

    

      HIBAG
      1.3.1
      1.2.4
      methods R
      R
      
      Xiuwen Zheng
    

    

      NB
      0.0.0.9000
      0.9
      R
      NaN
      
      Hui TinYu
    

    

      shape
      1.0.0
      1.4.2
      R
      R stats
      Kwame Okrah
      Karline Soetaert
    

    

      PANDA
      0.3
      0.9.9
      R
      GO.db cluster
      
      Hua
    

    

      RCMIP5
      1.1.9000
      1.1
      abind dplyr assertthat digest R
      abind dplyr reshape2 digest R
      
      Kathe ToddBrown
    

    

      jaatha
      2.99.0.9030
      2.7.0
      assertthat coala PopGenome R6 reshape2 paralle...
      phyclust Rcpp reshape2 parallel R methods
      
      Paul Staab
    

    

      lintr
      0.3.0.9000
      0.2.0
      rex crayon codetools stringdist testthat diges...
      rex crayon codetools stringdist testthat diges...
      
      Hester Jim
    

    

      taxize
      0.5.4.9320
      0.5.2
      XML RCurl reshape2 stringr plyr httr jsonlite ...
      XML RCurl stringr plyr httr jsonlite foreach a...
      
      Chamberlain Scott
    

    

      rnoaa
      0.3.5.99
      0.3.3
      httr lubridate plyr dplyr tidyr ggplot2 scales...
      httr lubridate plyr ggplot2 scales sp RCurl rg...
      
      Chamberlain Scott
    

    

      selectr
      0.1
      0.2-3
      NaN
      methods XML stringr R
      Janko Thyson
      Potter Simon
    

    

      mlr
      2.4
      2.3
      checkmate parallelMap plyr reshape2 survival R...
      checkmate parallelMap plyr reshape2 survival R...
      
      Bernd Bischl
    

    

      DNMF
      1.1
      1.0
      Matrix gplots parallel doParallel foreach
      NaN
      
      Jia Zhilong
    

    

      rentrez
      0.5.1
      0.4.1
      httr jsonlite XML R
      httr jsonlite R XML
      
      David Winter
    

    

      Gmisc
      1.0
      1.1
      NaN
      grid lattice Hmisc forestplot abind methods kn...
      Giuseppe Paleologo
      Gordon Max
    

    

      webchem
      0.0.2
      0.0.1
      XML RCurl jsonlite
      XML RCurl RJSONIO
      
      Eduard Szoecs
    

    

      gsheet
      0.2.0
      0.1.0
      rvest stringr magrittr
      dplyr rvest stringr
      
      Conway Max
    

    

      tab
      1.0.0
      3.1.1
      magrittr crayon methods
      survey survival gee R
      
      Dane Domelen Van
    

    

      repmis
      0.4.1
      0.4.2
      data.table digest httr plyr R.cache xlsx R
      data.table digest httr plyr R.cache R
      
      Christopher Gandrud
    

    

      flowr
      0.9.6.7
      0.9.6.5
      knitr ggplot2 RSQLite reshape2 openxlsx diagra...
      knitr ggplot2 RSQLite reshape2 methods stats d...
      
      Sahil Seth
    

    

      rivr
      0.0.0.9000
      0.9.2
      R6 R
      Rcpp reshape2 R
      
      Koohafkan Michael
    

    

      falsy
      1.0
      1.0.1
      NaN
      NaN
      
      Csardi Gabor
    

    

      ramify
      0.2.1.9999
      0.2.0
      NaN
      NaN
      
      Brandon Greenwell
    

    

      dga
      0.8
      1.2
      NaN
      chron
      Ball Patrick
      Kristian Lum
    

    

      bold
      0.2.2.99
      0.2.6
      XML httr stringr assertthat jsonlite reshape p...
      XML httr stringr assertthat jsonlite reshape plyr
      
      Chamberlain Scott
    

    

      treatSens
      1.2
      1.1
      methods stats graphics grDevices lme4 dbarts R
      R
      
      Bohme Carnegie Nicole
    

    

      RDML
      0.8
      0.8-4
      R6 assertthat dpcR plyr dplyr
      R6 assertthat XML plyr dplyr R
      
      Blagodatskikh Konstantin
    

    

      effsize
      0.5.5
      0.5.4
      NaN
      NaN
      
      Marco Torchiano
    

    

      genderizeR
      1.0.0.1
      1.1.0
      jsonlite stringr tm data.table magrittr RCurl ...
      stringr httr tm data.table magrittr parallel R
      
      Kamil Wais
    

    

      quickpsy
      0.1
      0.1.0
      boot DEoptim dplyr ggplot2 tidyr R
      MPDiR R boot DEoptim dplyr ggplot2 tidyr
      
      Daniel Linares
    

    

      gistr
      0.1.2.9999
      0.2.0
      jsonlite httr magrittr assertthat knitr rmarkdown
      jsonlite httr magrittr assertthat knitr rmarkd...
      
      Chamberlain Scott
    

    

      abc
      0.0.1.9000
      2.1
      R
      R abc.data nnet quantreg MASS locfit
      
      Blum Michael
    

    

      Deriv
      3.1
      3.4
      NaN
      NaN
      
      Serguei Sokol
    

    

      sdcMicroGUI
      1.1.6
      1.2.0
      vcd foreign Hmisc tools sdcMicro gWidgetsRGtk2...
      vcd foreign Hmisc tools sdcMicro gWidgetsRGtk2...
      Matthias Templ
      Alexander Kowarik
    

    

      mixedMem
      1.0.0
      1.0.2
      Rcpp gtools
      Rcpp gtools R
      
      Samuel Wang
    

    

      dmm
      0.1
      1.6-1
      httr xml2 RCurl
      MASS Matrix nadiv robustbase pls
      
      Jackson Neville
    

    

      rotationForest
      1.0
      0.1
      rpart R
      rpart
      Manan Shah
      Ballings Michel
    

    

      covr
      0.2.0.9000
      1.0.0
      jsonlite rex devtools httr testthat crayon R m...
      jsonlite rex devtools httr crayon htmltools R ...
      
      Hester Jim
    

    

      trip
      1.1-20
      1.1-21
      maptools MASS spatstat methods sp
      maptools MASS raster spatstat methods sp
      
      Michael Sumner
    

    

      meteoForecast
      0.44
      0.45
      ncdf rjson raster sp zoo
      ncdf raster sp zoo
      
      Lamigueiro Oscar Perpinan
    

    

      dpmr
      0.1.6
      0.1.7-1
      data.table digest httr jsonlite magrittr
      digest httr jsonlite magrittr rio
      
      Christopher Gandrud
    

    

      dendextendRcpp
      0.5.0
      0.6.1
      Rcpp dendextend
      R Rcpp dendextend
      
      Galili Tal
    

    

      plspm
      0.4.4
      0.4.7
      tester turner shape diagram amap R
      tester turner diagram shape amap R
      
      Gaston Sanchez
    

    

      knitcitations
      1.0.4
      1.0.6
      RefManageR digest httr R methods
      RefManageR digest httr methods R
      
      Boettiger Carl
    

    

      aroma.core
      2.13.0
      2.13.1
      utils R.methodsS3 R.oo R.cache R.rsp matrixSta...
      stats utils R.methodsS3 R.oo R.cache R.rsp mat...
      
      Bengtsson Henrik
    

    

      progress
      1.0.0
      1.0.1
      prettyunits magrittr R6
      prettyunits R6
      
      Csardi Gabor
    

    

      signal
      0.0.0.9000
      0.7-5
      R
      MASS graphics grDevices R stats
      
      Ligges Uwe

Content source: maelick/GitHub-Analysis

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