我正在用 Overleaf 写论文natbib。我还想让它显示我的全部参考书目，而不仅仅是我引用的那些。不幸的是，当使用它时，它只plainnat显示 3 个参考文献（即使我引用了更多），unsrtnat显示我引用的那些，而不是整个列表。这种不一致对我来说完全没有意义，因此我很困惑，找不到我的问题。奇怪的是，所有的引用都显示在文本中，但没有显示在参考书目中。
这是我的代码的一部分（整个代码太长了，我希望我包括了相关部分。如果没有，我很抱歉，这是我第一次在这里提问:)。大多数代码是由 Uni 提供的，所以我不知道是否有一些包发生冲突或其他问题。
\documentclass[11pt,twoside]{article}
\usepackage{geometry}
\usepackage{enumerate}
\usepackage{latexsym,booktabs}
\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\usepackage[authoryear,round]{natbib}
\usepackage[singlespacing]{setspace}
\usepackage{subcaption}
\usepackage{mwe}
\begin{document}
In the text some citing is happening as \cite{..} and \citep{}
\nocite{*}
\bibliography{library}
\bibliographystyle{plainnat}
\end{document}
这是我的图书馆的一部分，与 library.bib 位于同一文件夹中。
@Article{Barnard2000,
author = {Barnard, John and McCulloch, Robert and Meng, Xiao-Li},
journal = {Statistica Sinica},
title = {Modelling {Covariance} {Matrices} in {Terms} of {Standard} {Deviations} and {Correlations}, with {Application} {To} {Shrinkage}},
year = {2000},
month = oct,
pages = {1281--1311},
volume = {10},
abstract = {The covariance matrix plays an important role in statistical inference, yet modeling a covariance matrix is often a difficult task in practice due to its dimensionality and the non-negative definite constraint. In order to model a co-variance matrix effectively, it is typically broken down into components based on modeling considerations or mathematical convenience. Decompositions that have received recent research attention include variance components, spectral decompo-sition, Cholesky decomposition, and matrix logarithm. In this paper we study a statistically motivated decomposition which appears to be relatively unexplored for the purpose of modeling. We model a covariance matrix in terms of its correspond-ing standard deviations and correlation matrix. We discuss two general modeling situations where this approach is useful: shrinkage estimation of regression co-efficients, and a general location-scale model for both categorical and continuous variables. We present some simple choices for priors in terms of standard deviations and the correlation matrix, and describe a straightforward computational strategy for obtaining the posterior of the covariance matrix. We apply our method to real and simulated data sets in the context of shrinkage estimation.},
comment = {here find cov decomposition},
file = {Full Text PDF:https\://www.researchgate.net/profile/John_Barnard2/publication/228606344_Modelling_Covariance_Matrices_in_Terms_of_Standard_Deviations_and_Correlations_with_Application_To_Shrinkage/links/5416f4c50cf2788c4b35f7e7/Modelling-Covariance-Matrices-in-Terms-of-Standard-Deviations-and-Correlations-with-Application-To-Shrinkage.pdf:application/pdf;ResearchGate Link:https\://www.researchgate.net/publication/228606344_Modelling_Covariance_Matrices_in_Terms_of_Standard_Deviations_and_Correlations_with_Application_To_Shrinkage:},
}
@Article{Higham2002,
author = {Higham, Nicholas},
journal = {IMA Journal of Numerical Analysis},
title = {Computing the {Nearest} {Correlation} {Matrix} - a {Problem} {From} {Finance}},
year = {2002},
month = oct,
volume = {22},
abstract = {Introduction A correlation matrix is a symmetric positive semidefinite matrix with unit diagonal. Correlation matrices occur in several areas of numerical linear algebra, including preconditioning of linear systems and error analysis of Jacobi methods for the symmetric eigenvalue problem (see Davies \& Higham (2000) for details and references). The term `correlation matrix' comes from statistics, since a matrix whose (i, j ) entry is the correlation coefficient between two random variables x i and x j is symmetric positive semidefinite with unit diagonal. It is a statistical application that motivates this work---one coming from the finance industry. In stock research sample correlation matrices constructed from vectors of stock returns are used for predictive purposes. Unfortunately, on any day when an observation is made data are rarely available for all the stocks of interest. One way to deal with this problem is to compute the sample correlations of pairs of stocks using data draw},
doi = {10.1093/imanum/22.3.329},
file = {ResearchGate Link:https\://www.researchgate.net/publication/2839645_Computing_the_Nearest_Correlation_Matrix_-_a_Problem_From_Finance:;:Masterarbeit/narep369.pdf:PDF},
}
奇怪的是，Bernard 的论文出现在参考文献中，但 Higham 的论文却没有。
我希望这个问题是可以理解的，如果我遗漏了你需要回答的内容，请告诉我（就像我说的，我在这里提问还是个新手）。非常感谢。