Our automated technical analysis of togaware.com website and it's covering network is reported that providing information technology services, seems like offering educational material.
Table Of Contents
Page | Time to First Byte | Status |
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ggraptr – Togaware / | 1008.41 ms | HTTP/200 |
Togaware – Resources for the Data Scientist /index.html | 898.99 ms | HTTP/200 |
ggraptr – Togaware /tag/ggraptr/index.html | 1018.66 ms | HTTP/200 |
linux – Togaware /tag/linux/index.html | 1018.05 ms | HTTP/200 |
leaflet – Togaware /tag/leaflet/index.html | 1017.66 ms | HTTP/200 |
introductions – Togaware /tag/introductions/index.html | 1017.09 ms | HTTP/200 |
information builders – Togaware /tag/information-builders/index.html | 1016.77 ms | HTTP/200 |
grammar of machine learning – Togaware /tag/grammar-of-machine-learning/index.html | 1016.49 ms | HTTP/200 |
graml – Togaware /tag/graml/index.html | 1016.29 ms | HTTP/200 |
government – Togaware /tag/government/index.html | 1016 ms | HTTP/200 |
ggplot2 – Togaware /tag/ggplot2/index.html | 1015.75 ms | HTTP/200 |
mchine learning – Togaware /tag/mchine-learning/index.html | 1015.45 ms | HTTP/200 |
feature requests – Togaware /tag/feature-requests/index.html | 1014.7 ms | HTTP/200 |
extreme ensembles – Togaware /tag/extreme-ensembles/index.html | 1014.33 ms | HTTP/200 |
ensembles – Togaware /tag/ensembles/index.html | 1013.65 ms | HTTP/200 |
data science – Togaware /tag/data-science/index.html | 1013.43 ms | HTTP/200 |
data import – Togaware /tag/data-import/index.html | 1012.81 ms | HTTP/200 |
connect-r – Togaware /tag/connect-r/index.html | 1016.22 ms | HTTP/200 |
australian – Togaware /tag/australian/index.html | 1015.88 ms | HTTP/200 |
analytics space – Togaware /tag/analytics-space/index.html | 1015.57 ms | HTTP/200 |
massively distributed models – Togaware /tag/massively-distributed-models/index.html | 1015.25 ms | HTTP/200 |
Microsoft R Server – Togaware /tag/microsoft-r-server/index.html | 1015.01 ms | HTTP/200 |
Togaware – Togaware /category/togaware/index.html | 1014.77 ms | HTTP/200 |
r_software – Togaware /tag/r_software/index.html | 1014.52 ms | HTTP/200 |
Togaware /feed/index.html | 1014.3 ms | HTTP/200 |
/wp-login.php.html | 905.54 ms | HTTP/200 |
web site – Togaware /tag/web-site/index.html | 1119.61 ms | HTTP/200 |
virtual machine – Togaware /tag/virtual-machine/index.html | 1119.46 ms | HTTP/200 |
video – Togaware /tag/video/index.html | 1119.22 ms | HTTP/200 |
spss – Togaware /tag/spss/index.html | 1119 ms | HTTP/200 |
shiny – Togaware /tag/shiny/index.html | 1118.83 ms | HTTP/200 |
sas – Togaware /tag/sas/index.html | 1118.65 ms | HTTP/200 |
rstat – Togaware /tag/rstat/index.html | 1118.42 ms | HTTP/200 |
model export – Togaware /tag/model-export/index.html | 1118.19 ms | HTTP/200 |
r software – Togaware /tag/r-software/index.html | 1117.99 ms | HTTP/200 |
rexer – Togaware /tag/rexer/index.html | 1117.79 ms | HTTP/200 |
rattle – Togaware /tag/rattle/index.html | 1117.57 ms | HTTP/200 |
raptr – Togaware /tag/raptr/index.html | 1117.42 ms | HTTP/200 |
R – Togaware /tag/r/index.html | 1117.28 ms | HTTP/200 |
privacy – Togaware /tag/privacy/index.html | 1117.13 ms | HTTP/200 |
open street map – Togaware /tag/open-street-map/index.html | 1116.94 ms | HTTP/200 |
open source – Togaware /tag/open-source/index.html | 1116.76 ms | HTTP/200 |
analytics – Togaware /tag/analytics/index.html | 1116.62 ms | HTTP/200 |
Rattle – Togaware /category/rattle/index.html | 1116.47 ms | HTTP/200 |
Graham’s Blog – Togaware /togaware/index.html | 1116.39 ms | HTTP/200 |
References – Togaware /index.html%3Fp=189.html | 1221.98 ms | HTTP/200 |
Publications – Togaware /index.html%3Fp=258.html | 1221.85 ms | HTTP/200 |
Presentations – Togaware /index.html%3Fp=314.html | 1221.68 ms | HTTP/200 |
Bio – Togaware /index.html%3Fp=274.html | 1221.54 ms | HTTP/200 |
Short Bio – Togaware /data-science-resources/short-bio/index.html | 1221.86 ms | HTTP/200 |
Graham Williams – Togaware /graham.williams.html | 1221.69 ms | HTTP/200 |
Togaware – Resources for the Data Scientist /index.html%3Fs=.html | 1008.17 ms | HTTP/200 |
ggraptr – Togaware /../../togaware/index.html | 1007.73 ms | HTTP/200 |
linux – Togaware /../../index.html%3Fp=189.html | 1007.52 ms | HTTP/200 |
leaflet – Togaware /../../index.html%3Fp=258.html | 1007.37 ms | HTTP/200 |
introductions – Togaware /../../index.html%3Fp=314.html | 1007.21 ms | HTTP/200 |
information builders – Togaware /../../index.html%3Fp=274.html | 1007.09 ms | HTTP/200 |
grammar of machine learning – Togaware /../../graham.williams.html | 1006.95 ms | HTTP/200 |
graml – Togaware /../../index.html%3Fp=160.html | 1006.81 ms | HTTP/200 |
government – Togaware /../../index.html%3Fp=137.html | 1006.67 ms | HTTP/200 |
ggplot2 – Togaware /../../index.html%3Fp=179.html | 1006.55 ms | HTTP/200 |
mchine learning – Togaware /../../onepager.html | 1006.38 ms | HTTP/200 |
feature requests – Togaware /../../2017/09/index.html | 1006.19 ms | HTTP/200 |
extreme ensembles – Togaware /../../index.html%3Fp=149.html | 1006.06 ms | HTTP/200 |
ensembles – Togaware /../../index.html%3Fp=182.html | 1005.93 ms | HTTP/200 |
data science – Togaware /../../index.html%3Fp=168.html | 1005.81 ms | HTTP/200 |
data import – Togaware /../../index.html%3Fp=177.html | 1005.66 ms | HTTP/200 |
connect-r – Togaware /../../index.html%3Fp=165.html | 1005.54 ms | HTTP/200 |
australian – Togaware /../../projects/rattle/index.html | 1005.38 ms | HTTP/200 |
analytics space – Togaware /../../index.html%3Fp=353.html | 1005.27 ms | HTTP/200 |
massively distributed models – Togaware /../../index.html | 1005.14 ms | HTTP/200 |
Microsoft R Server – Togaware /../../2017/08/index.html | 1005.04 ms | HTTP/200 |
Togaware – Togaware /../../2015/09/index.html | 1004.93 ms | HTTP/200 |
r_software – Togaware /../../comments/feed/index.html | 899.41 ms | HTTP/200 |
Togaware /../../feed/index.html | 1004.55 ms | HTTP/200 |
/../../wp-login.php.html | 899.19 ms | HTTP/200 |
web site – Togaware /../../index.html%3Fp=515.html | 1004.21 ms | HTTP/200 |
virtual machine – Togaware /../../index.html%3Fp=497.html | 1004.1 ms | HTTP/200 |
video – Togaware /../../index.html%3Fp=850.html | 1004 ms | HTTP/200 |
spss – Togaware /../../index.html%3Fp=870.html | 1003.89 ms | HTTP/200 |
shiny – Togaware /../../index.html%3Fp=890.html | 1003.79 ms | HTTP/200 |
sas – Togaware /../../2014/04/index.html | 1003.67 ms | HTTP/200 |
rstat – Togaware /../../2014/07/index.html | 1003.55 ms | HTTP/200 |
model export – Togaware /../../2014/11/index.html | 1108.64 ms | HTTP/200 |
r software – Togaware /../../2015/10/index.html | 1108.62 ms | HTTP/200 |
rexer – Togaware /../../2017/05/index.html | 1108.56 ms | HTTP/200 |
rattle – Togaware /../../2015/11/index.html | 1108.46 ms | HTTP/200 |
raptr – Togaware /../../2016/07/index.html | 1108.4 ms | HTTP/200 |
R – Togaware /../../2016/09/index.html | 1108.4 ms | HTTP/200 |
privacy – Togaware /../../2016/11/index.html | 1108.23 ms | HTTP/200 |
open street map – Togaware /../../2016/12/index.html | 1108.28 ms | HTTP/200 |
open source – Togaware /../../data-science-resources/short-bio/index.html | 1108.36 ms | HTTP/200 |
analytics – Togaware /../../author/gjw/index.html | 1108.29 ms | HTTP/200 |
Rattle – Togaware /../../category/rattle/index.html | 1108.18 ms | HTTP/200 |
Graham’s Blog – Togaware /../../category/cloud/index.html | 1108.3 ms | HTTP/200 |
References – Togaware /../../category/conference/index.html | 1108.21 ms | HTTP/200 |
Publications – Togaware /../../category/togaware/index.html | 1108.31 ms | HTTP/200 |
Presentations – Togaware /../../category/general/index.html | 1108.39 ms | HTTP/200 |
Bio – Togaware /../../category/microsoft/index.html | 1108.49 ms | HTTP/200 |
Short Bio – Togaware /../rattle/index.html | 1108.46 ms | HTTP/200 |
Graham Williams – Togaware /../graml/index.html | 999.77 ms | HTTP/200 |
Togaware – Resources for the Data Scientist /../shiny/index.html | 918.39 ms | HTTP/404 |
ggraptr – Togaware /../open-source/index.html | 917.93 ms | HTTP/404 |
linux – Togaware /../sas/index.html | 917.96 ms | HTTP/404 |
leaflet – Togaware /../ggraptr/index.html | 917.9 ms | HTTP/404 |
introductions – Togaware /../web-site/index.html | 917.82 ms | HTTP/404 |
information builders – Togaware /../virtual-machine/index.html | 917.78 ms | HTTP/404 |
grammar of machine learning – Togaware /../open-street-map/index.html | 917.7 ms | HTTP/404 |
graml – Togaware /../spss/index.html | 917.62 ms | HTTP/404 |
government – Togaware /../r_software/index.html | 917.52 ms | HTTP/404 |
ggplot2 – Togaware /../video/index.html | 917.45 ms | HTTP/404 |
mchine learning – Togaware /../rstat/index.html | 917.37 ms | HTTP/404 |
feature requests – Togaware /../r-software/index.html | 917.22 ms | HTTP/404 |
extreme ensembles – Togaware /../rexer/index.html | 917.13 ms | HTTP/404 |
ensembles – Togaware /../raptr/index.html | 917.05 ms | HTTP/404 |
data science – Togaware /../r/index.html | 916.96 ms | HTTP/404 |
data import – Togaware /../privacy/index.html | 916.85 ms | HTTP/404 |
connect-r – Togaware /../microsoft-r-server/index.html | 916.76 ms | HTTP/404 |
australian – Togaware /../mchine-learning/index.html | 916.67 ms | HTTP/404 |
analytics space – Togaware /../data-import/index.html | 916.59 ms | HTTP/404 |
massively distributed models – Togaware /../analytics/index.html | 916.5 ms | HTTP/404 |
Microsoft R Server – Togaware /../grammar-of-machine-learning/index.html | 916.4 ms | HTTP/404 |
Togaware – Togaware /../analytics-space/index.html | 916.31 ms | HTTP/404 |
r_software – Togaware /../ggplot2/index.html | 916.23 ms | HTTP/404 |
Togaware /../massively-distributed-models/index.html | 916.14 ms | HTTP/404 |
Page Redirection /../linux/index.html | 916.08 ms | HTTP/404 |
web site – Togaware /../leaflet/index.html | 916.04 ms | HTTP/404 |
virtual machine – Togaware /../introductions/index.html | 915.98 ms | HTTP/404 |
video – Togaware /../information-builders/index.html | 915.92 ms | HTTP/404 |
spss – Togaware /../government/index.html | 915.82 ms | HTTP/404 |
shiny – Togaware /../model-export/index.html | 915.75 ms | HTTP/404 |
sas – Togaware /../extreme-ensembles/index.html | 915.68 ms | HTTP/404 |
rstat – Togaware /../ensembles/index.html | 915.59 ms | HTTP/404 |
model export – Togaware /../data-science/index.html | 1016.38 ms | HTTP/404 |
r software – Togaware /../connect-r/index.html | 1016.58 ms | HTTP/404 |
rexer – Togaware /../feature-requests/index.html | 1016.77 ms | HTTP/404 |
rattle – Togaware /../australian/index.html | 1016.73 ms | HTTP/404 |
raptr – Togaware /index.html%3Fp=149.html | 1117.43 ms | HTTP/404 |
R – Togaware /index.html%3Fp=515.html | 1117.34 ms | HTTP/404 |
privacy – Togaware /category/general/index.html | 1117.22 ms | HTTP/404 |
open street map – Togaware /2017/08/index.html | 1117.18 ms | HTTP/404 |
open source – Togaware /index.html%3Fp=137.html | 1117.13 ms | HTTP/404 |
analytics – Togaware /index.html%3Fp=179.html | 1117.05 ms | HTTP/404 |
Rattle – Togaware /index.html%3Fp=890.html | 1116.95 ms | HTTP/404 |
Graham’s Blog – Togaware /index.html%3Fp=353.html | 1116.83 ms | HTTP/404 |
References – Togaware /index.html%3Fp=165.html | 1116.8 ms | HTTP/404 |
Publications – Togaware /index.html%3Fp=182.html | 1116.64 ms | HTTP/404 |
Presentations – Togaware /index.html%3Fp=168.html | 1116.52 ms | HTTP/404 |
Bio – Togaware /index.html%3Fp=177.html | 1116.4 ms | HTTP/404 |
Short Bio – Togaware /projects/rattle/index.html | 1116.28 ms | HTTP/404 |
Graham Williams – Togaware /onepager.html | 1116.15 ms | HTTP/404 |
Togaware – Resources for the Data Scientist /category/cloud/index.html | 1014.28 ms | HTTP/200 |
ggraptr – Togaware /index.html%3Fp=160.html | 1013.91 ms | HTTP/200 |
linux – Togaware /2016/09/index.html | 1013.76 ms | HTTP/200 |
leaflet – Togaware /2014/04/index.html | 1013.69 ms | HTTP/200 |
introductions – Togaware /category/conference/index.html | 1013.58 ms | HTTP/200 |
information builders – Togaware /2016/12/index.html | 1013.45 ms | HTTP/200 |
grammar of machine learning – Togaware /2017/05/index.html | 1013.34 ms | HTTP/200 |
graml – Togaware /2016/11/index.html | 1013.28 ms | HTTP/200 |
government – Togaware /index.html%3Fp=850.html | 1013.15 ms | HTTP/200 |
ggplot2 – Togaware /2016/07/index.html | 1013.02 ms | HTTP/200 |
mchine learning – Togaware /2015/10/index.html | 1012.92 ms | HTTP/200 |
feature requests – Togaware /2015/09/index.html | 1012.81 ms | HTTP/200 |
extreme ensembles – Togaware /2014/11/index.html | 1012.71 ms | HTTP/200 |
ensembles – Togaware /2014/07/index.html | 1012.58 ms | HTTP/200 |
data science – Togaware /index.html%3Fp=870.html | 1012.48 ms | HTTP/200 |
data import – Togaware /2017/09/index.html | 1012.42 ms | HTTP/200 |
connect-r – Togaware /category/microsoft/index.html | 1012.28 ms | HTTP/200 |
australian – Togaware /comments/feed/index.html | 906.69 ms | HTTP/200 |
analytics space – Togaware /index.html%3Fp=497.html | 1011.87 ms | HTTP/200 |
massively distributed models – Togaware /2015/11/index.html | 1011.77 ms | HTTP/200 |
Microsoft R Server – Togaware /../../the-azure-linux-data-science-virtual-machine/index.html | 1011.65 ms | HTTP/200 |
Togaware – Togaware /../../papers/ieee_award_2017.pdf | 1011.57 ms | HTTP/200 |
r_software – Togaware /../../papers/cim14.pdf | 1011.5 ms | HTTP/200 |
Togaware /../../index.html%3Fp=671.html | 1011.41 ms | HTTP/200 |
Page Redirection /../../index.html%3Fp=530.html | 1011.32 ms | HTTP/200 |
web site – Togaware /../../index.html%3Fp=115.html | 1011.13 ms | HTTP/200 |
virtual machine – Togaware /../../wp-content/uploads/2015/10/Screenshot-raptR-Mozilla-Firefox.png | 1011.28 ms | HTTP/200 |
video – Togaware /../../index.html%3Fp=420.html | 1011.17 ms | HTTP/200 |
spss – Togaware /../../index.html%3Fp=319.html | 1011.07 ms | HTTP/200 |
shiny – Togaware ...10/Screenshot-Rexer-Data-Science-Survey-Highlights-Sep-2015.pdf-Adobe-Reader.png | 1011.01 ms | HTTP/200 |
sas – Togaware /../../index.html%3Fp=426.html | 1010.93 ms | HTTP/200 |
rstat – Togaware /../../wp-content/uploads/2016/07/Screenshot-from-2016-07-29-091441.png | 1010.86 ms | HTTP/200 |
model export – Togaware /../../index.html%3Fp=123.html | 1115.82 ms | HTTP/200 |
r software – Togaware /../../index.html%3Fp=413.html | 1115.78 ms | HTTP/200 |
rexer – Togaware ..../../wp-content/uploads/2015/11/Screenshot-R-Data-Miner-Rattle-weather.csv-1.png | 1115.89 ms | HTTP/200 |
rattle – Togaware /../../wp-content/uploads/2016/07/Screenshot-from-2016-10-04-203025.png | 1115.83 ms | HTTP/200 |
raptr – Togaware /../../wp-content/uploads/2016/07/Screenshot-from-2016-10-04-200651.png | 1115.71 ms | HTTP/200 |
R – Togaware /../../wp-content/uploads/2016/07/Screenshot-from-2016-07-29-091549.png | 1115.54 ms | HTTP/200 |
privacy – Togaware /../../tag/microsoft-r-server/index.html | 1115.7 ms | HTTP/200 |
open street map – Togaware /../../tag/introductions/index.html | 1115.55 ms | HTTP/200 |
open source – Togaware /../../tag/mchine-learning/index.html | 1115.4 ms | HTTP/200 |
analytics – Togaware /../../tag/spss/index.html | 1115.26 ms | HTTP/200 |
Rattle – Togaware /../../tag/r/index.html | 1115.18 ms | HTTP/200 |
Graham’s Blog – Togaware /../../tag/shiny/index.html | 1115.43 ms | HTTP/200 |
References – Togaware /../../tag/open-source/index.html | 1115.28 ms | HTTP/200 |
Publications – Togaware /../../tag/open-street-map/index.html | 1115.19 ms | HTTP/200 |
Presentations – Togaware /../../tag/privacy/index.html | 1115.97 ms | HTTP/200 |
Bio – Togaware /../../tag/linux/index.html | 1115.87 ms | HTTP/200 |
Short Bio – Togaware /../../tag/r-software/index.html | 1115.73 ms | HTTP/200 |
Graham Williams – Togaware /../../tag/rstat/index.html | 1115.61 ms | HTTP/200 |
Togaware – Resources for the Data Scientist /../../wp-content/uploads/2016/09/Screenshot-from-2016-09-12-130554.png | 1029.79 ms | HTTP/200 |
ggraptr – Togaware /../../index.html%3Fp=492.html | 1029.39 ms | HTTP/200 |
linux – Togaware /../../wp-content/uploads/2016/09/Screenshot-from-2016-09-12-130305.png | 1029.39 ms | HTTP/200 |
leaflet – Togaware /../../tag/grammar-of-machine-learning/index.html | 1029.7 ms | HTTP/200 |
introductions – Togaware /../../tag/analytics-space/index.html | 1029.61 ms | HTTP/200 |
information builders – Togaware /../../wp-content/uploads/2016/09/Screenshot-from-2016-09-12-131401.png | 1029.53 ms | HTTP/200 |
grammar of machine learning – Togaware /../../tag/model-export/index.html | 1029.46 ms | HTTP/200 |
graml – Togaware /../../wp-content/uploads/2016/09/Screenshot-from-2016-09-12-130617.png | 1029.45 ms | HTTP/200 |
government – Togaware /../../index.html%3Fp=139.html | 1029.34 ms | HTTP/200 |
ggplot2 – Togaware /../../tag/graml/index.html | 1029.26 ms | HTTP/200 |
mchine learning – Togaware /../../tag/government/index.html | 1029.18 ms | HTTP/200 |
feature requests – Togaware /../../tag/data-import/index.html | 1029.13 ms | HTTP/200 |
extreme ensembles – Togaware /../../index.html%3Fp=574.html | 1029.05 ms | HTTP/200 |
ensembles – Togaware /../../tag/australian/index.html | 1028.98 ms | HTTP/200 |
data science – Togaware /../../tag/connect-r/index.html | 1028.91 ms | HTTP/200 |
data import – Togaware /../../tag/data-science/index.html | 1028.86 ms | HTTP/200 |
connect-r – Togaware /../../tag/ensembles/index.html | 1028.85 ms | HTTP/200 |
australian – Togaware /../../tag/extreme-ensembles/index.html | 1028.81 ms | HTTP/200 |
analytics space – Togaware /../../tag/feature-requests/index.html | 1028.81 ms | HTTP/200 |
massively distributed models – Togaware /../../tag/ggplot2/index.html | 1028.72 ms | HTTP/200 |
Microsoft R Server – Togaware /../../tag/ggraptr/index.html | 1028.58 ms | HTTP/200 |
Togaware – Togaware /../../tag/analytics/index.html | 1028.51 ms | HTTP/200 |
r_software – Togaware /../../wp-content/uploads/2016/09/Screenshot-from-2016-09-12-124814.png | 1028.37 ms | HTTP/200 |
Togaware /../../tag/massively-distributed-models/index.html | 1028.33 ms | HTTP/200 |
Page Redirection /../../tag/leaflet/index.html | 1028.29 ms | HTTP/200 |
web site – Togaware /../../tag/raptr/index.html | 1028.21 ms | HTTP/200 |
virtual machine – Togaware /../../tag/rattle/index.html | 1028.14 ms | HTTP/200 |
video – Togaware /../../tag/rexer/index.html | 1028.13 ms | HTTP/200 |
spss – Togaware /../../tag/r_software/index.html | 1028.07 ms | HTTP/200 |
shiny – Togaware /../../tag/sas/index.html | 1027.94 ms | HTTP/200 |
sas – Togaware /../../tag/information-builders/index.html | 1027.78 ms | HTTP/200 |
rstat – Togaware /../../tag/web-site/index.html | 1133.23 ms | HTTP/200 |
model export – Togaware /../../tag/virtual-machine/index.html | 1133.22 ms | HTTP/200 |
r software – Togaware /../../tag/video/index.html | 1133.21 ms | HTTP/200 |
rexer – Togaware /../../index.html%3Fp=126.html | 1133.08 ms | HTTP/200 |
rattle – Togaware /../microsoft/index.html | 1026.87 ms | HTTP/200 |
raptr – Togaware /../cloud/index.html | 1026.87 ms | HTTP/200 |
R – Togaware /../general/index.html | 1026.82 ms | HTTP/200 |
privacy – Togaware /papers/ai97.pdf | 1132.23 ms | HTTP/200 |
open street map – Togaware /../conference/index.html | 1026.53 ms | HTTP/200 |
open source – Togaware /papers/ieee_award_2017.pdf | 1131.86 ms | HTTP/200 |
analytics – Togaware /papers/milai87.pdf | 1131.73 ms | HTTP/200 |
Rattle – Togaware /page/2/index.html | 1026.04 ms | HTTP/200 |
Graham’s Blog – Togaware /index.html%3Fp=115.html | 1131.43 ms | HTTP/200 |
References – Togaware /wp-content/uploads/2016/09/Screenshot-from-2016-09-12-130305.png | 1131.31 ms | HTTP/200 |
Publications – Togaware /rattle/ | 1131.37 ms | HTTP/200 |
Presentations – Togaware /the-azure-linux-data-science-virtual-machine/ | 1324.73 ms | HTTP/200 |
Bio – Togaware /wp-content/uploads/2016/07/Screenshot-from-2016-07-29-091441.png | 1131.5 ms | HTTP/200 |
Short Bio – Togaware /wp-content/uploads/2016/07/Screenshot-from-2016-10-04-200651.png | 1131.53 ms | HTTP/200 |
Graham Williams – Togaware /wp-content/uploads/2016/07/Screenshot-from-2016-07-29-091549.png | 1242.76 ms | HTTP/200 |
Togaware – Resources for the Data Scientist /papers/RJournal_2009-2_Williams.pdf | 1008.82 ms | HTTP/200 |
linux – Togaware /Graham.Williams.html | 1008.37 ms | HTTP/200 |
leaflet – Togaware /papers/cim14.pdf | 1008.27 ms | HTTP/200 |
introductions – Togaware /papers/hwrf12.pdf | 1008.08 ms | HTTP/200 |
information builders – Togaware /papers/rfpakdd12.pdf | 975.06 ms | HTTP/200 |
grammar of machine learning – Togaware /papers/story.pdf | 1008.33 ms | HTTP/200 |
graml – Togaware /papers/kes05.pdf | 1008.25 ms | HTTP/200 |
government – Togaware /papers/kdd05.pdf | 1008.05 ms | HTTP/200 |
ggplot2 – Togaware /papers/ijdwm2012.pdf | 1007.99 ms | HTTP/200 |
mchine learning – Togaware /about/publications/index.html%3Fs=.html | 1007.91 ms | HTTP/200 |
feature requests – Togaware /papers/RJournal_2009-1_Guazzelli+et+al.pdf | 1007.7 ms | HTTP/200 |
extreme ensembles – Togaware /papers/icdm08.pdf | 1007.6 ms | HTTP/200 |
ensembles – Togaware /papers/titb08.pdf | 1007.44 ms | HTTP/200 |
data science – Togaware /papers/pakdd08.pdf | 1007.21 ms | HTTP/200 |
data import – Togaware /wp-content/uploads/2015/10/background1.jpg | 1006.98 ms | HTTP/200 |
connect-r – Togaware /papers/pakdd07.pdf | 1007.01 ms | HTTP/200 |
australian – Togaware /papers/miningmodels.pdf | 1006.97 ms | HTTP/200 |
analytics space – Togaware /papers/eJHI06.pdf | 1006.82 ms | HTTP/200 |
massively distributed models – Togaware /papers/ausdm07.pdf | 1006.79 ms | HTTP/200 |
Microsoft R Server – Togaware /papers/kes05full.pdf | 1006.79 ms | HTTP/200 |
Togaware – Togaware /../tag/r-software/index.html | 1006.62 ms | HTTP/200 |
r_software – Togaware /../tag/rstat/index.html | 1006.48 ms | HTTP/200 |
Togaware /papers/pakdd99.pdf | 1006.41 ms | HTTP/200 |
Page Redirection /papers/kais09.fdf | 1135.34 ms | HTTP/200 |
web site – Togaware /../tag/open-source/index.html | 1006.48 ms | HTTP/200 |
virtual machine – Togaware /../tag/open-street-map/index.html | 1006.36 ms | HTTP/200 |
video – Togaware /../tag/privacy/index.html | 1006.24 ms | HTTP/200 |
spss – Togaware /../tag/r/index.html | 1006.24 ms | HTTP/200 |
shiny – Togaware /../tag/raptr/index.html | 1006.22 ms | HTTP/200 |
sas – Togaware /../tag/rattle/index.html | 1006.27 ms | HTTP/200 |
rstat – Togaware /../tag/leaflet/index.html | 1006.14 ms | HTTP/200 |
model export – Togaware /papers/pakdd01.pdf | 1108.41 ms | HTTP/200 |
r software – Togaware /papers/kdd00.pdf | 1108.3 ms | HTTP/200 |
rexer – Togaware /../tag/sas/index.html | 1108.28 ms | HTTP/200 |
rattle – Togaware /papers/hdm05.pdf | 1108.15 ms | HTTP/200 |
raptr – Togaware /../tag/shiny/index.html | 1108.49 ms | HTTP/200 |
R – Togaware /../tag/spss/index.html | 1108.49 ms | HTTP/200 |
privacy – Togaware /../tag/video/index.html | 1108.79 ms | HTTP/200 |
open street map – Togaware /../tag/virtual-machine/index.html | 1108.66 ms | HTTP/200 |
open source – Togaware /../tag/web-site/index.html | 1108.52 ms | HTTP/200 |
analytics – Togaware /../wp-login.php.html | 1004.15 ms | HTTP/200 |
Rattle – Togaware /../feed/index.html | 1108.16 ms | HTTP/200 |
Graham’s Blog – Togaware /../tag/mchine-learning/index.html | 1107.99 ms | HTTP/200 |
References – Togaware /data-science-resources/press/index.html%3Fs=.html | 1107.95 ms | HTTP/200 |
Publications – Togaware /../tag/rexer/index.html | 1107.9 ms | HTTP/200 |
Presentations – Togaware /papers/dmkd03.pdf | 1108.26 ms | HTTP/200 |
Bio – Togaware /papers/seal98.pdf | 1108.24 ms | HTTP/200 |
Short Bio – Togaware /../tag/analytics-space/index.html | 1108.19 ms | HTTP/200 |
Graham Williams – Togaware /assets/20211213_fmfi2021_accessible.pdf | 1108.05 ms | HTTP/200 |
Togaware – Resources for the Data Scientist /setting-up-r-for-a-tutorial/ | 1023.27 ms | HTTP/200 |
ggraptr – Togaware /wp-content/uploads/2016/07/Screenshot-from-2016-10-04-203025.png | 1021.97 ms | HTTP/200 |
linux – Togaware /wp-content/uploads/2015/11/Screenshot-R-Data-Miner-Rattle-weather.csv-1.png | 1021.83 ms | HTTP/200 |
leaflet – Togaware /wp-content/uploads/2016/09/Screenshot-from-2016-09-12-124814.png | 1021.65 ms | HTTP/200 |
introductions – Togaware /wp-content/uploads/2016/09/Screenshot-from-2016-09-12-130617.png | 1021.49 ms | HTTP/200 |
information builders – Togaware /../index.html | 1021.34 ms | HTTP/200 |
grammar of machine learning – Togaware /wp-content/uploads/2016/09/Screenshot-from-2016-09-12-130554.png | 1021.19 ms | HTTP/200 |
graml – Togaware /essentials-of-data-science/ | 1020.88 ms | HTTP/200 |
government – Togaware /wp-content/uploads/2016/09/Screenshot-from-2016-09-12-131401.png | 1020.52 ms | HTTP/200 |
ggplot2 – Togaware /../togaware/index.html | 1020.05 ms | HTTP/200 |
mchine learning – Togaware /../../index.html%3Fp=698.html | 1019.71 ms | HTTP/200 |
feature requests – Togaware /../../index.html%3Fp=439.html | 1019.47 ms | HTTP/200 |
extreme ensembles – Togaware /../../index.html%3Fp=441.html | 1019.41 ms | HTTP/200 |
ensembles – Togaware /../../wp-content/uploads/2015/11/Screenshot-Rattle-Plot-3.png | 1019.2 ms | HTTP/200 |
data science – Togaware /../../index.html%3Fp=433.html | 1018.96 ms | HTTP/200 |
data import – Togaware /../index.html%3Fp=497.html | 1018.6 ms | HTTP/200 |
connect-r – Togaware /../category/cloud/index.html | 1018.35 ms | HTTP/200 |
australian – Togaware /../tag/ggplot2/index.html | 1018.38 ms | HTTP/200 |
analytics space – Togaware /../tag/feature-requests/index.html | 1018.06 ms | HTTP/200 |
Network informations and whois lookup for togaware.com
Contact | SOA Serial |
---|---|
hostmaster@togaware.com | 2022041703 |
IP | Location | Provider |
---|---|---|
209.58.165.79 opal2.opalstack.com | Asia / Singapore | Leaseweb Asia Pacific pte. ltd. |
IP | Location | Provider |
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IP/Target | Location | Provider |
---|---|---|
23.82.16.244 mx2.us.opalstack.com | North America / United States | LEASEWEB-USA-SFO-12 |
108.62.123.121 mx1.us.opalstack.com | North America / United States | LEASEWEB-USA-WDC-01 |
IP | Location | Provider |
---|---|---|
45.79.107.55 ns1.us.opalstack.com | North America / United States | Linode, LLC |
23.92.21.232 ns2.us.opalstack.com | North America / United States | Linode, LLC |
45.56.74.5 ns3.us.opalstack.com | North America / United States | Linode, LLC |
E-Mail Account Settings for togaware.com
Host | Port | Security Settings |
---|---|---|
mx2.us.opalstack.com | 25 | STARTTLS / TLS (Secure) Port 25 is usually blocked by many ISP, setting this port is not recommended! |
Host | Port | Security Settings |
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Host | Port | Security Settings |
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This session was sponsored by nPulse.net (AI) - the most advanced website analytics software
First Contentful Paint ( 25 / 100 )
First Contentful Paint marks the time at which the first text or image is painted.
3.9 s |
Speed Index ( 50 / 100 )
Speed Index shows how quickly the contents of a page are visibly populated.
5.8 s |
Largest Contentful Paint ( 47 / 100 )
Largest Contentful Paint marks the time at which the largest text or image is painted.
4.1 s |
Time to Interactive ( 57 / 100 )
Time to interactive is the amount of time it takes for the page to become fully interactive.
6.7 s |
First Meaningful Paint ( 51 / 100 )
First Meaningful Paint measures when the primary content of a page is visible.
3.9 s |
Eliminate render-blocking resources ( 72 / 100 )
Resources are blocking the first paint of your page. Consider delivering critical JS/CSS inline and deferring all non-critical JS/styles.
Potential savings of 350 ms |
Minify JavaScript ( 57 / 100 )
Minifying JavaScript files can reduce payload sizes and script parse time.
Potential savings of 55 KiB |
Reduce unused CSS ( 74 / 100 )
Reduce unused rules from stylesheets and defer CSS not used for above-the-fold content to decrease bytes consumed by network activity.
Potential savings of 35 KiB |
Reduce unused JavaScript ( 57 / 100 )
Reduce unused JavaScript and defer loading scripts until they are required to decrease bytes consumed by network activity.
Potential savings of 100 KiB |
Serve images in next-gen formats ( 74 / 100 )
Image formats like WebP and AVIF often provide better compression than PNG or JPEG, which means faster downloads and less data consumption.
Potential savings of 26 KiB |
Enable text compression ( 48 / 100 )
Text-based resources should be served with compression (gzip, deflate or brotli) to minimize total network bytes.
Potential savings of 187 KiB |
Avoid serving legacy JavaScript to modern browsers ( 74 / 100 )
Polyfills and transforms enable legacy browsers to use new JavaScript features. However, many aren't necessary for modern browsers. For your bundled JavaScript, adopt a modern script deployment strategy using module/nomodule feature detection to reduce the amount of code shipped to modern browsers, while retaining support for legacy browsers. [Learn More](https://philipwalton.com/articles/deploying-es2015-code-in-production-today/)
Potential savings of 19 KiB |
Serve static assets with an efficient cache policy ( 11 / 100 )
A long cache lifetime can speed up repeat visits to your page.
102 resources found |
Ensure text remains visible during webfont load ( 0 / 100 )
Leverage the font-display CSS feature to ensure text is user-visible while webfonts are loading.
Total Blocking Time ( 97 / 100 )
Sum of all time periods between FCP and Time to Interactive, when task length exceeded 50ms, expressed in milliseconds.
110 ms |
Cumulative Layout Shift ( 100 / 100 )
Cumulative Layout Shift measures the movement of visible elements within the viewport.
0.006 |
Max Potential First Input Delay ( 84 / 100 )
The maximum potential First Input Delay that your users could experience is the duration of the longest task.
150 ms |
Properly size images ( 100 / 100 )
Serve images that are appropriately-sized to save cellular data and improve load time.
Defer offscreen images ( 100 / 100 )
Consider lazy-loading offscreen and hidden images after all critical resources have finished loading to lower time to interactive.
Minify CSS ( 100 / 100 )
Minifying CSS files can reduce network payload sizes.
Potential savings of 3 KiB |
Efficiently encode images ( 100 / 100 )
Optimized images load faster and consume less cellular data.
Preconnect to required origins ( 95 / 100 )
Consider adding `preconnect` or `dns-prefetch` resource hints to establish early connections to important third-party origins.
Potential savings of 60 ms |
Initial server response time was short ( 100 / 100 )
Keep the server response time for the main document short because all other requests depend on it.
Root document took 180 ms |
Avoid multiple page redirects ( 100 / 100 )
Redirects introduce additional delays before the page can be loaded.
Potential savings of 960 ms |
Preload key requests
Consider using `<link rel=preload>` to prioritize fetching resources that are currently requested later in page load.
Use HTTP/2 ( 100 / 100 )
HTTP/2 offers many benefits over HTTP/1.1, including binary headers and multiplexing.
Use video formats for animated content ( 100 / 100 )
Large GIFs are inefficient for delivering animated content. Consider using MPEG4/WebM videos for animations and PNG/WebP for static images instead of GIF to save network bytes.
Remove duplicate modules in JavaScript bundles ( 100 / 100 )
Remove large, duplicate JavaScript modules from bundles to reduce unnecessary bytes consumed by network activity.
Preload Largest Contentful Paint image
Preload the image used by the LCP element in order to improve your LCP time.
Avoids enormous network payloads ( 100 / 100 )
Large network payloads cost users real money and are highly correlated with long load times.
Total size was 589 KiB |
Avoids an excessive DOM size ( 100 / 100 )
A large DOM will increase memory usage, cause longer [style calculations](https://developers.google.com/web/fundamentals/performance/rendering/reduce-the-scope-and-complexity-of-style-calculations), and produce costly [layout reflows](https://developers.google.com/speed/articles/reflow).
383 elements |
Avoid chaining critical requests
The Critical Request Chains below show you what resources are loaded with a high priority. Consider reducing the length of chains, reducing the download size of resources, or deferring the download of unnecessary resources to improve page load.
102 chains found |
User Timing marks and measures
Consider instrumenting your app with the User Timing API to measure your app's real-world performance during key user experiences.
JavaScript execution time ( 99 / 100 )
Consider reducing the time spent parsing, compiling, and executing JS. You may find delivering smaller JS payloads helps with this.
0.6 s |
Minimize main-thread work ( 81 / 100 )
Consider reducing the time spent parsing, compiling and executing JS. You may find delivering smaller JS payloads helps with this.
2.5 s |
Performance budget
Keep the quantity and size of network requests under the targets set by the provided performance budget.
Timing budget
Set a timing budget to help you keep an eye on the performance of your site. Performant sites load fast and respond to user input events quickly.
Keep request counts low and transfer sizes small
To set budgets for the quantity and size of page resources, add a budget.json file.
107 requests • 589 KiB |
Minimize third-party usage ( 100 / 100 )
Third-party code can significantly impact load performance. Limit the number of redundant third-party providers and try to load third-party code after your page has primarily finished loading.
Third-party code blocked the main thread for 0 ms |
Lazy load third-party resources with facades
Some third-party embeds can be lazy loaded. Consider replacing them with a facade until they are required.
Largest Contentful Paint element
This is the largest contentful element painted within the viewport. [Learn More](https://web.dev/lighthouse-largest-contentful-paint/)
1 element found |
Avoid large layout shifts
These DOM elements contribute most to the CLS of the page.
3 elements found |
Uses passive listeners to improve scrolling performance ( 100 / 100 )
Consider marking your touch and wheel event listeners as `passive` to improve your page's scroll performance.
Avoids `document.write()` ( 100 / 100 )
For users on slow connections, external scripts dynamically injected via `document.write()` can delay page load by tens of seconds.
Avoid long main-thread tasks
Lists the longest tasks on the main thread, useful for identifying worst contributors to input delay.
7 long tasks found |
Avoid non-composited animations
Animations which are not composited can be janky and increase CLS.
Image elements have explicit `width` and `height` ( 100 / 100 )
Set an explicit width and height on image elements to reduce layout shifts and improve CLS.
Network Requests
Lists the network requests that were made during page load.
Network Round Trip Times
Network round trip times (RTT) have a large impact on performance. If the RTT to an origin is high, it's an indication that servers closer to the user could improve performance.
170 ms |
Server Backend Latencies
Server latencies can impact web performance. If the server latency of an origin is high, it's an indication the server is overloaded or has poor backend performance.
170 ms |
Tasks
Lists the toplevel main thread tasks that executed during page load.
Diagnostics
Collection of useful page vitals.
Metrics
Collects all available metrics.
Screenshot Thumbnails
This is what the load of your site looked like.
Final Screenshot
The last screenshot captured of the pageload.
Script Treemap Data
Used for treemap app
Background and foreground colors do not have a sufficient contrast ratio. ( 0 / 100 )
Low-contrast text is difficult or impossible for many users to read.
Heading elements are not in a sequentially-descending order ( 0 / 100 )
Properly ordered headings that do not skip levels convey the semantic structure of the page, making it easier to navigate and understand when using assistive technologies.
`[accesskey]` values are unique
Access keys let users quickly focus a part of the page. For proper navigation, each access key must be unique.
`[aria-*]` attributes match their roles ( 100 / 100 )
Each ARIA `role` supports a specific subset of `aria-*` attributes. Mismatching these invalidates the `aria-*` attributes.
`button`, `link`, and `menuitem` elements have accessible names
When an element doesn't have an accessible name, screen readers announce it with a generic name, making it unusable for users who rely on screen readers.
`[aria-hidden="true"]` is not present on the document `<body>` ( 100 / 100 )
Assistive technologies, like screen readers, work inconsistently when `aria-hidden="true"` is set on the document `<body>`.
`[aria-hidden="true"]` elements do not contain focusable descendents ( 100 / 100 )
Focusable descendents within an `[aria-hidden="true"]` element prevent those interactive elements from being available to users of assistive technologies like screen readers.
ARIA input fields have accessible names
When an input field doesn't have an accessible name, screen readers announce it with a generic name, making it unusable for users who rely on screen readers.
ARIA `meter` elements have accessible names
When an element doesn't have an accessible name, screen readers announce it with a generic name, making it unusable for users who rely on screen readers.
ARIA `progressbar` elements have accessible names
When a `progressbar` element doesn't have an accessible name, screen readers announce it with a generic name, making it unusable for users who rely on screen readers.
`[role]`s have all required `[aria-*]` attributes ( 100 / 100 )
Some ARIA roles have required attributes that describe the state of the element to screen readers.
Elements with an ARIA `[role]` that require children to contain a specific `[role]` have all required children.
Some ARIA parent roles must contain specific child roles to perform their intended accessibility functions.
`[role]`s are contained by their required parent element
Some ARIA child roles must be contained by specific parent roles to properly perform their intended accessibility functions.
`[role]` values are valid ( 100 / 100 )
ARIA roles must have valid values in order to perform their intended accessibility functions.
ARIA toggle fields have accessible names
When a toggle field doesn't have an accessible name, screen readers announce it with a generic name, making it unusable for users who rely on screen readers.
ARIA `tooltip` elements have accessible names
When an element doesn't have an accessible name, screen readers announce it with a generic name, making it unusable for users who rely on screen readers.
ARIA `treeitem` elements have accessible names
When an element doesn't have an accessible name, screen readers announce it with a generic name, making it unusable for users who rely on screen readers.
`[aria-*]` attributes have valid values ( 100 / 100 )
Assistive technologies, like screen readers, can't interpret ARIA attributes with invalid values.
`[aria-*]` attributes are valid and not misspelled ( 100 / 100 )
Assistive technologies, like screen readers, can't interpret ARIA attributes with invalid names.
Buttons have an accessible name
When a button doesn't have an accessible name, screen readers announce it as "button", making it unusable for users who rely on screen readers.
The page contains a heading, skip link, or landmark region ( 100 / 100 )
Adding ways to bypass repetitive content lets keyboard users navigate the page more efficiently.
`<dl>`'s contain only properly-ordered `<dt>` and `<dd>` groups, `<script>`, `<template>` or `<div>` elements.
When definition lists are not properly marked up, screen readers may produce confusing or inaccurate output.
Definition list items are wrapped in `<dl>` elements
Definition list items (`<dt>` and `<dd>`) must be wrapped in a parent `<dl>` element to ensure that screen readers can properly announce them.
Document has a `<title>` element ( 100 / 100 )
The title gives screen reader users an overview of the page, and search engine users rely on it heavily to determine if a page is relevant to their search.
`[id]` attributes on active, focusable elements are unique ( 100 / 100 )
All focusable elements must have a unique `id` to ensure that they're visible to assistive technologies.
ARIA IDs are unique
The value of an ARIA ID must be unique to prevent other instances from being overlooked by assistive technologies.
No form fields have multiple labels
Form fields with multiple labels can be confusingly announced by assistive technologies like screen readers which use either the first, the last, or all of the labels.
`<frame>` or `<iframe>` elements have a title
Screen reader users rely on frame titles to describe the contents of frames.
`<html>` element has a `[lang]` attribute ( 100 / 100 )
If a page doesn't specify a lang attribute, a screen reader assumes that the page is in the default language that the user chose when setting up the screen reader. If the page isn't actually in the default language, then the screen reader might not announce the page's text correctly.
`<html>` element has a valid value for its `[lang]` attribute ( 100 / 100 )
Specifying a valid [BCP 47 language](https://www.w3.org/International/questions/qa-choosing-language-tags#question) helps screen readers announce text properly.
Image elements have `[alt]` attributes ( 100 / 100 )
Informative elements should aim for short, descriptive alternate text. Decorative elements can be ignored with an empty alt attribute.
`<input type="image">` elements have `[alt]` text
When an image is being used as an `<input>` button, providing alternative text can help screen reader users understand the purpose of th