A review of integrated analysis in fisheries stock assessment
Limited data, and the requirement to provide science-based advice for exploited
populations, have led to the development of statistical methods that combine several …
populations, have led to the development of statistical methods that combine several …
A guide to state–space modeling of ecological time series
State–space models (SSMs) are an important modeling framework for analyzing ecological
time series. These hierarchical models are commonly used to model population dynamics …
time series. These hierarchical models are commonly used to model population dynamics …
The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded
The coefficient of determination R 2 quantifies the proportion of variance explained by a
statistical model and is an important summary statistic of biological interest. However …
statistical model and is an important summary statistic of biological interest. However …
TMB: automatic differentiation and Laplace approximation
TMB is an open source R package that enables quick implementation of complex nonlinear
random effects (latent variable) models in a manner similar to the established AD Model …
random effects (latent variable) models in a manner similar to the established AD Model …
Using observation-level random effects to model overdispersion in count data in ecology and evolution
XA Harrison - PeerJ, 2014 - peerj.com
Overdispersion is common in models of count data in ecology and evolutionary biology, and
can occur due to missing covariates, non-independent (aggregated) data, or an excess …
can occur due to missing covariates, non-independent (aggregated) data, or an excess …
Monte carlo gradient estimation in machine learning
This paper is a broad and accessible survey of the methods we have at our disposal for
Monte Carlo gradient estimation in machine learning and across the statistical sciences: the …
Monte Carlo gradient estimation in machine learning and across the statistical sciences: the …
Dynamic norms promote sustainable behavior, even if it is counternormative
It is well known that people conform to normative information about other people's current
attitudes and behaviors. Do they also conform to dynamic norms—information about how …
attitudes and behaviors. Do they also conform to dynamic norms—information about how …
Gaia Data Release 3-Catalogue validation
C Babusiaux, C Fabricius, S Khanna… - Astronomy & …, 2023 - aanda.org
Context. The third Gaia data release (DR3) provides a wealth of new data products. The
early part of the release, Gaia EDR3, already provided the astrometric and photometric data …
early part of the release, Gaia EDR3, already provided the astrometric and photometric data …
Programming with models: writing statistical algorithms for general model structures with NIMBLE
We describe NIMBLE, a system for programming statistical algorithms for general model
structures within R. NIMBLE is designed to meet three challenges: flexible model …
structures within R. NIMBLE is designed to meet three challenges: flexible model …
[BOOK][B] Hidden Markov models for time series: an introduction using R
W Zucchini, IL MacDonald - 2009 - taylorfrancis.com
Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all
methods in RHidden Markov Models for Time Series: An Introduction Using R applies …
methods in RHidden Markov Models for Time Series: An Introduction Using R applies …