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All papers authored by Thomas Yee

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2023 Thomas W. Yee

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Taylor’s power law and reduced-rank vector generalized linear models

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2022 Sofie E. Voerman, Beauregard C. Marsh, Ricardo G. Bahia, Guilherme H. Pereira-Filho, Thomas W. Yee

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, Ana Clara F. Becker, Gilberto M. Amado-Filho, Arvydas Ruseckas

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, Graham A. Turnbull

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, Ifor D. W. Samuel

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, Heidi L. Burdett

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Ecosystem engineer morphological traits and taxon identity shape biodiversity across the euphotic–mesophotic transition

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2021 Thomas W. Yee

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On the Hauck-Donner Effect in Wald Tests: Detection, Tipping Points and Parameter Space Characterization

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2020 Thomas W. Yee

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The VGAM package for negative binomial regression

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2019 V. F. Miranda-Soberanis

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, T. W. Yee

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New Link Functions for Distribution–Specific Quantile Regression Based on Vector Generalized Linear and Additive Models

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2015 Thomas W. Yee

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Vector Generalized Linear and Additive Models

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Thomas W. Yee

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, Jakub Stoklosa, Richard M. Huggins

TheVGAMPackage for Capture-Recapture Data Using the Conditional Likelihood

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T. W. Yee

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, J. Stoklosa, R. M. Huggins

The VGAM package for capture-recapture data using the conditional likelihood

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T. W. Yee

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Vector Generalized Linear and Additive Models: With an Implementation in R

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2014 Thomas W. Yee

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, Alfian F. Hadi

Row–column interaction models, with an R implementation

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Thomas W. Yee

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Reduced-rank vector generalized linear models with two linear predictors

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Thomas W. Yee

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Scoring rules, and the role of chance: Analysis of the 2008 World Fly Fishing Championships

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2010 Thomas W. Yee

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VGLMs and VGAMs: An overview for applications in fisheries research

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Thomas W. Yee

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TheVGAMPackage for Categorical Data Analysis

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T. W. Yee

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The VGAM package for categorical data analysis

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2009 Thomas W. Yee

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, Thomas Dirnböck

Models for analysing species' presence/absence data at two time points

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2007 Tw Yee

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, Ag Stephenson

Vector generalized linear and additive extreme value models

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2006 A. H. Welsh, T. W. Yee

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Local regression for vector responses

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Tw Yee

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Constrained additive ordination

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2004 Thomas W. Yee

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A new technique for maximum-likelihood canonical Gaussian ordination

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