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Determining minimal output sets that ensure structural identifiability
Determining minimal output sets that ensure structural identifiability
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Determining minimal output sets that ensure structural identifiability

Joubert, D ; Stigter, J D ; Molenaar, J Srinivasan, Manoj

PloS one, 2018-11, Vol.13 (11), p.e0207334-e0207334 [Periódico revisado por pares]

United States: Public Library of Science

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2
Statistical models discriminating between complex samples measured with microfluidic receptor-cell arrays
Statistical models discriminating between complex samples measured with microfluidic receptor-cell arrays
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Statistical models discriminating between complex samples measured with microfluidic receptor-cell arrays

Wehrens, Ron ; Roelse, Margriet ; Henquet, Maurice ; van Lenthe, Marco ; Goedhart, Paul W ; Jongsma, Maarten A Eddington, David T.

PloS one, 2019-04, Vol.14 (4), p.e0214878-e0214878 [Periódico revisado por pares]

United States: Public Library of Science

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3
SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data
SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data
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SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data

Varet, Hugo ; Brillet-Guéguen, Loraine ; Coppée, Jean-Yves ; Dillies, Marie-Agnès Mills, Ken

PloS one, 2016-06, Vol.11 (6), p.e0157022-e0157022 [Periódico revisado por pares]

United States: Public Library of Science

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4
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
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MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites

Esteban, Oscar ; Birman, Daniel ; Schaer, Marie ; Koyejo, Oluwasanmi O ; Poldrack, Russell A ; Gorgolewski, Krzysztof J Bernhardt, Boris C

PloS one, 2017-09, Vol.12 (9), p.e0184661-e0184661 [Periódico revisado por pares]

United States: Public Library of Science

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5
Statistical and Machine Learning forecasting methods: Concerns and ways forward
Statistical and Machine Learning forecasting methods: Concerns and ways forward
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Statistical and Machine Learning forecasting methods: Concerns and ways forward

Makridakis, Spyros ; Spiliotis, Evangelos ; Assimakopoulos, Vassilios Hernandez Montoya, Alejandro Raul

PloS one, 2018-03, Vol.13 (3), p.e0194889-e0194889 [Periódico revisado por pares]

United States: Public Library of Science

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6
The relation between statistical power and inference in fMRI
The relation between statistical power and inference in fMRI
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The relation between statistical power and inference in fMRI

Cremers, Henk R ; Wager, Tor D ; Yarkoni, Tal Gilbert, Sam

PloS one, 2017-11, Vol.12 (11), p.e0184923-e0184923 [Periódico revisado por pares]

United States: Public Library of Science

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7
Statistical inference methods for two crossing survival curves: a comparison of methods
Statistical inference methods for two crossing survival curves: a comparison of methods
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Statistical inference methods for two crossing survival curves: a comparison of methods

Li, Huimin ; Han, Dong ; Hou, Yawen ; Chen, Huilin ; Chen, Zheng Chen, Zhongxue

PloS one, 2015-01, Vol.10 (1), p.e0116774-e0116774 [Periódico revisado por pares]

United States: Public Library of Science

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8
Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard - An update
Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard - An update
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Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard - An update

Umemneku Chikere, Chinyereugo M ; Wilson, Kevin ; Graziadio, Sara ; Vale, Luke ; Allen, A Joy Virgili, Gianni

PloS one, 2019-10, Vol.14 (10), p.e0223832-e0223832 [Periódico revisado por pares]

United States: Public Library of Science

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9
Powerlaw: a Python package for analysis of heavy-tailed distributions
Powerlaw: a Python package for analysis of heavy-tailed distributions
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Powerlaw: a Python package for analysis of heavy-tailed distributions

Alstott, Jeff ; Bullmore, Ed ; Plenz, Dietmar Rapallo, Fabio

PloS one, 2014-01, Vol.9 (1), p.e85777-e85777 [Periódico revisado por pares]

United States: Public Library of Science

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10
The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses
The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses
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The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses

Warton, David I ; Thibaut, Loïc ; Wang, Yi Alice Politis, Dimitris N.

PloS one, 2017-07, Vol.12 (7), p.e0181790-e0181790 [Periódico revisado por pares]

United States: Public Library of Science

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