Elemental Abundances of Cool Stars: A Spectroscopic Framework Applied to Five Planet-Host Stars. Nicole Gromek et. al. https://arxiv.org/abs/2609.24970
![[$ $/Fe] versus [Fe/H] for the five stars in this work (red stars) compared to a sample of 31 nearby M dwarfs {Jahandar_2025](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:32r7scd5hucgv552zjfuaigc/bafkreidbhujuliemycksegggaqwnoykqp4mvtb53slxuvdz6rhizylnmbi)
![C/O as a function of Mg/Si for the five stars in this work (red stars) compared with [][green circles]{Jahandar_2025](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:32r7scd5hucgv552zjfuaigc/bafkreifoyxegtb6di32vncddtophqxyx5zyg4hea34s534winptsz7aj4a)
![Kernel density estimates of the [Fe/H] distributions for the high-weight (solid) and low-weight (dashed) halves of each star's Fe I linelist. Vertical lines indicate the corresponding mean abundances for each subset of lines.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:32r7scd5hucgv552zjfuaigc/bafkreibobz6u7khvwzxjmipctvt4ctpaghby7vtfzhx4acbiycmjhxlczu)
September 22, 2026 at 7:02 AM
Elemental Abundances of Cool Stars: A Spectroscopic Framework Applied to Five Planet-Host Stars. Nicole Gromek et. al. https://arxiv.org/abs/2609.24970
High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies https://arxiv.org/abs/2607.10250
July 15, 2026 at 12:30 AM
High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies https://arxiv.org/abs/2607.10250
CRAN updates: care4cmodel linelist pedprobr #rstats
February 28, 2024 at 6:02 PM
CRAN updates: care4cmodel linelist pedprobr #rstats
[2607.10250] Rafael Rianço-Silva et al.: High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies. link
July 14, 2026 at 3:48 AM
[2607.10250] Rafael Rianço-Silva et al.: High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies. link
Rafael Rian\c{c}o-Silva, Pedro Machado, Clara Sousa Silva, Sergey Yurchenko, Giovanna Tinetti: High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies https://arxiv.org/abs/2607.10250 https://arxiv.org/pdf/2607.10250 https://arxiv.org/html/2607.10250
July 14, 2026 at 6:51 AM
Rafael Rian\c{c}o-Silva, Pedro Machado, Clara Sousa Silva, Sergey Yurchenko, Giovanna Tinetti: High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies https://arxiv.org/abs/2607.10250 https://arxiv.org/pdf/2607.10250 https://arxiv.org/html/2607.10250
High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies
https://arxiv.org/pdf/2607.10250
Rafael Rianço-Silva, Pedro Machado, Clara Sousa Silva, Sergey Yurchenko, Giovanna Tinetti.
https://arxiv.org/pdf/2607.10250
Rafael Rianço-Silva, Pedro Machado, Clara Sousa Silva, Sergey Yurchenko, Giovanna Tinetti.
https://arxiv.org/abs/2607.10250
arXiv abstract link
arxiv.org
July 14, 2026 at 4:02 AM
High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies
https://arxiv.org/pdf/2607.10250
Rafael Rianço-Silva, Pedro Machado, Clara Sousa Silva, Sergey Yurchenko, Giovanna Tinetti.
https://arxiv.org/pdf/2607.10250
Rafael Rianço-Silva, Pedro Machado, Clara Sousa Silva, Sergey Yurchenko, Giovanna Tinetti.
Using Bayesian inference on linelist + restaurant order records, we estimated:
1) Attack rate: 60.3% (95% CrI 48.0–71.3)
2) Case-fatality ratio (CFR): 20.0% (8.6–34.3)
3) Time from dining to seeking healthcare: 3.4 d (hospitalized) vs 5.6 d (non-hospitalized)
1) Attack rate: 60.3% (95% CrI 48.0–71.3)
2) Case-fatality ratio (CFR): 20.0% (8.6–34.3)
3) Time from dining to seeking healthcare: 3.4 d (hospitalized) vs 5.6 d (non-hospitalized)
April 30, 2026 at 7:55 AM
Using Bayesian inference on linelist + restaurant order records, we estimated:
1) Attack rate: 60.3% (95% CrI 48.0–71.3)
2) Case-fatality ratio (CFR): 20.0% (8.6–34.3)
3) Time from dining to seeking healthcare: 3.4 d (hospitalized) vs 5.6 d (non-hospitalized)
1) Attack rate: 60.3% (95% CrI 48.0–71.3)
2) Case-fatality ratio (CFR): 20.0% (8.6–34.3)
3) Time from dining to seeking healthcare: 3.4 d (hospitalized) vs 5.6 d (non-hospitalized)
Updates on CRAN: admiraldev (1.3.1), groupcompare (1.0.1), lares (5.3.0), linelist (2.0.1), ordinalsimr (0.2.2), PepMapViz (1.1.0), smoothr (1.1.0), vol2birdR (1.1.1)
June 25, 2025 at 9:25 PM
Updates on CRAN: admiraldev (1.3.1), groupcompare (1.0.1), lares (5.3.0), linelist (2.0.1), ordinalsimr (0.2.2), PepMapViz (1.1.0), smoothr (1.1.0), vol2birdR (1.1.1)
Updates on CRAN: AzureQstor (1.0.2), iccde (0.3.8), linelist (2.0.0), medfate (4.8.1), mets (1.3.6), rdeps (0.3), RStanTVA (0.2.6)
April 23, 2025 at 9:27 AM
Updates on CRAN: AzureQstor (1.0.2), iccde (0.3.8), linelist (2.0.0), medfate (4.8.1), mets (1.3.6), rdeps (0.3), RStanTVA (0.2.6)
Updates on CRAN: admiral (1.1.1), archeoViz (1.3.5), CohortSymmetry (0.1.1), FRK (2.3.0), lamW (2.2.4), linelist (1.1.4), parallelPlot (0.4.0), refugees (2023.12.0), scatterPlotMatrix (0.3.0)
June 17, 2024 at 5:18 PM
Updates on CRAN: admiral (1.1.1), archeoViz (1.3.5), CohortSymmetry (0.1.1), FRK (2.3.0), lamW (2.2.4), linelist (1.1.4), parallelPlot (0.4.0), refugees (2023.12.0), scatterPlotMatrix (0.3.0)
Updates on CRAN: allometric (2.3.0), CRE (0.2.6), DeclareDesign (1.0.10), flexsurv (2.3), flps (1.1.0), gdi (1.6.0), glmnetr (0.4-6), INLAspacetime (0.1.8), linelist (1.1.3), openssl (2.1.2), rEMM (1.2.1), rtpcr (1.0.5), stream (2.0-2), uwot (0.2.2)
April 22, 2024 at 1:55 AM
Updates on CRAN: allometric (2.3.0), CRE (0.2.6), DeclareDesign (1.0.10), flexsurv (2.3), flps (1.1.0), gdi (1.6.0), glmnetr (0.4-6), INLAspacetime (0.1.8), linelist (1.1.3), openssl (2.1.2), rEMM (1.2.1), rtpcr (1.0.5), stream (2.0-2), uwot (0.2.2)
Updates on CRAN: antaresEditObject (0.6.2), bioregion (1.1.1), espadon (1.7.0), linelist (1.1.2), QWDAP (1.1.18), segmented (2.0-4)
April 19, 2024 at 1:17 PM
Updates on CRAN: antaresEditObject (0.6.2), bioregion (1.1.1), espadon (1.7.0), linelist (1.1.2), QWDAP (1.1.18), segmented (2.0-4)
Updates on CRAN: apexcharter (0.4.2), care4cmodel (1.0.1), datamods (1.4.5), DImodels (1.3.2), ibmsunburst (0.1.4), linelist (1.1.0), pedprobr (0.9.2), RcppEigen (0.3.4.0.0), scCustomize (2.1.2)
February 28, 2024 at 9:16 PM
Updates on CRAN: apexcharter (0.4.2), care4cmodel (1.0.1), datamods (1.4.5), DImodels (1.3.2), ibmsunburst (0.1.4), linelist (1.1.0), pedprobr (0.9.2), RcppEigen (0.3.4.0.0), scCustomize (2.1.2)
High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies
📄 https://arxiv.org/abs/2607.10250v1
📖 Summary: https://arifsolmaz.github.io/arxiv?date=2026-07-14#paper-2607-10250v1
📄 https://arxiv.org/abs/2607.10250v1
📖 Summary: https://arifsolmaz.github.io/arxiv?date=2026-07-14#paper-2607-10250v1
July 14, 2026 at 11:23 AM
High Resolution Optical Methane Linelist from observations of Titan for Cross-Correlation studies
📄 https://arxiv.org/abs/2607.10250v1
📖 Summary: https://arifsolmaz.github.io/arxiv?date=2026-07-14#paper-2607-10250v1
📄 https://arxiv.org/abs/2607.10250v1
📖 Summary: https://arifsolmaz.github.io/arxiv?date=2026-07-14#paper-2607-10250v1
#linelist and all Epiverse packages are:
🔹 Open-source and community-driven
🔹 Developed by epidemiologists and data scientists
🔹 Designed for seamless interoperability within outbreak response pipelines
#epiverseTRACE #outbreakanalytics #infectiousdiseasemodelling #epidemiology #rstats
3/3
🔹 Open-source and community-driven
🔹 Developed by epidemiologists and data scientists
🔹 Designed for seamless interoperability within outbreak response pipelines
#epiverseTRACE #outbreakanalytics #infectiousdiseasemodelling #epidemiology #rstats
3/3
April 8, 2025 at 2:54 PM
#linelist and all Epiverse packages are:
🔹 Open-source and community-driven
🔹 Developed by epidemiologists and data scientists
🔹 Designed for seamless interoperability within outbreak response pipelines
#epiverseTRACE #outbreakanalytics #infectiousdiseasemodelling #epidemiology #rstats
3/3
🔹 Open-source and community-driven
🔹 Developed by epidemiologists and data scientists
🔹 Designed for seamless interoperability within outbreak response pipelines
#epiverseTRACE #outbreakanalytics #infectiousdiseasemodelling #epidemiology #rstats
3/3
#linelist, one of Epiverse's Digital Public Goods, is a great entry point into our open-source ecosystem for outbreak analytics.
🔗 Explore {linelist}: epiverse-trace.github.io/linelist/
💬 Already using #linelist? We’d love to hear about your experience: epiversecomms@data.org
2/3
🔗 Explore {linelist}: epiverse-trace.github.io/linelist/
💬 Already using #linelist? We’d love to hear about your experience: epiversecomms@data.org
2/3
Tagging and Validating Epidemiological Data
Provides tools to help storing and handling case line list data. The linelist class adds a tagging system to classical data.frame objects to identify key epidemiological data such as dates of symptom ...
epiverse-trace.github.io
April 8, 2025 at 2:54 PM
#linelist, one of Epiverse's Digital Public Goods, is a great entry point into our open-source ecosystem for outbreak analytics.
🔗 Explore {linelist}: epiverse-trace.github.io/linelist/
💬 Already using #linelist? We’d love to hear about your experience: epiversecomms@data.org
2/3
🔗 Explore {linelist}: epiverse-trace.github.io/linelist/
💬 Already using #linelist? We’d love to hear about your experience: epiversecomms@data.org
2/3
📦 R Package of the Week: {linelist}
Turn data frames into #linelist objects by:
✅ Tagging key epidemiological variables (so you're not tied to column names)
✅ Validation to ensure tagged variables are of the correct type
✅ Safeguard against accidental loss of tagged variables during wrangling
1/3
Turn data frames into #linelist objects by:
✅ Tagging key epidemiological variables (so you're not tied to column names)
✅ Validation to ensure tagged variables are of the correct type
✅ Safeguard against accidental loss of tagged variables during wrangling
1/3
April 8, 2025 at 2:54 PM
📦 R Package of the Week: {linelist}
Turn data frames into #linelist objects by:
✅ Tagging key epidemiological variables (so you're not tied to column names)
✅ Validation to ensure tagged variables are of the correct type
✅ Safeguard against accidental loss of tagged variables during wrangling
1/3
Turn data frames into #linelist objects by:
✅ Tagging key epidemiological variables (so you're not tied to column names)
✅ Validation to ensure tagged variables are of the correct type
✅ Safeguard against accidental loss of tagged variables during wrangling
1/3
Maybe in the linelist for the cases Gh_ID 16, 17 and 18 in the column linked_information you could put in what country they were evacuated to?
May 11, 2026 at 11:35 AM
Maybe in the linelist for the cases Gh_ID 16, 17 and 18 in the column linked_information you could put in what country they were evacuated to?
And finally, if you want the function code pasted above, you can get it from the Applied Epi Community: community.appliedepi.org/t/custom-ggp...
Enjoy!
#RStats #Dataviz
Enjoy!
#RStats #Dataviz
Custom ggplot functions!
Hi all! I’m pasting in some example code to create a custom ggplot function. This example creates different epicurves using the Ebola linelist from Applied Epi’s introcourse. It allows you to defin...
community.appliedepi.org
December 22, 2024 at 12:24 AM
And finally, if you want the function code pasted above, you can get it from the Applied Epi Community: community.appliedepi.org/t/custom-ggp...
Enjoy!
#RStats #Dataviz
Enjoy!
#RStats #Dataviz
So there you have it. It might seem arbitrary, but this can be a real issue when cleaning strings in a linelist! Things that look the same might not be the same, but there will be a solution! 💪
December 15, 2024 at 11:09 PM
So there you have it. It might seem arbitrary, but this can be a real issue when cleaning strings in a linelist! Things that look the same might not be the same, but there will be a solution! 💪