@article{canova2012institutional,
  title = {Do {{Institutional Changes Affect Business Cycles}}? {{Evidence}} from {{Europe}}},
  shorttitle = {Do Institutional Changes Affect Business Cycles?},
  author = {Canova, Fabio and Ciccarelli, Matteo and Ortega, Eva},
  year = {2012},
  month = oct,
  journal = {Journal of Economic Dynamics and Control},
  volume = {36},
  number = {10},
  pages = {1520--1533},
  issn = {0165-1889},
  doi = {10.1016/j.jedc.2012.03.017},
  urldate = {2015-02-05},
  abstract = {We study the effects that the Maastricht Treaty, the creation of the ECB, and the Euro changeover had on the dynamics of European business cycles using a panel VAR and data from 10 European countries---seven from the Euro area and three outside of it. There are changes in the features of European business cycles and in the transmission of shocks. They precede the three events of interest and are more linked to a general process of European convergence and synchronization.},
  keywords = {Business cycles,European Monetary Union,Panel VAR,Structural changes},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\ctsem manual\\Canova et al_2012_Do Institutional Changes Affect Business Cycles.pdf;C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\ctsem manual\\Canova et al_2012_Do Institutional Changes Affect Business Cycles2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\GCKEUPD9\\S0165188912000942.html;C\:\\Users\\Driver\\Zotero\\storage\\JBA5XWPT\\S0165188912000942.html}
}

@article{carpenter2017stan,
  title = {Stan: A Probabilistic Programming Language},
  shorttitle = {{\emph{Stan}}},
  author = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
  year = {2017},
  journal = {Journal of Statistical Software},
  volume = {76},
  number = {1},
  issn = {1548-7660},
  doi = {10.18637/jss.v076.i01},
  urldate = {2017-09-01},
  langid = {english},
  keywords = {algorithmic differentiation,Bayesian inference,probabilistic programming,Stan},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\heirarchical ct article\\Carpenter et al_2017_Stan.pdf;C\:\\Users\\Driver\\Zotero\\storage\\CGJNAJKA\\v076i01.html}
}

@article{driver2017continuous,
  title = {Continuous {{Time Structural Equation Modeling}} with {{R}} Package Ctsem},
  author = {Driver, Charles C. and Oud, Johan H. L. and Voelkle, Manuel C.},
  year = {2017},
  journal = {Journal of Statistical Software},
  volume = {77},
  number = {5},
  pages = {1--35},
  issn = {1548-7660},
  doi = {10.18637/jss.v077.i05},
  abstract = {We introduce ctsem, an R package for continuous time structural equation modeling of panel (N {\textquestiondown} 1) and time series (N = 1) data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models) in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem.},
  keywords = {continuous time,dynamic models,Dynamic models,Kalman filter,longitudinal modeling,Longitudinal modeling,panel data,Panel data,R,state space,stochastic differential equation,structural equation modeling,time series},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\Driver et al_2017_Continuous time structural equation modeling with r package ctsem.pdf;C\:\\Users\\Driver\\Zotero\\storage\\LQLGLHWW\\Driver et al_2017_Continuous time structural equation modeling with r package ctsem.pdf}
}

@article{driver2018hierarchicala,
  title = {Hierarchical {{Bayesian}} Continuous Time Dynamic Modeling},
  author = {Driver, Charles C and Voelkle, Manuel C},
  year = {2018},
  journal = {Psychological Methods},
  volume = {23},
  number = {4},
  pages = {774--799},
  publisher = {American Psychological Association},
  doi = {10.1037/met0000168},
  keywords = {Bayes Theorem,Humans,Individuality,Models Psychological,Models Statistical,Psychology}
}

@incollection{driver2018understanding,
  title = {Understanding the Time Course of Interventions with Continuous Time Dynamic Models},
  booktitle = {Continuous Time Modeling in the Behavioral and Related Sciences},
  author = {Driver, Charles C. and Voelkle, Manuel C.},
  editor = {van Montfort, Kees and Oud, Johan H. L. and Voelkle, Manuel C.},
  year = {2018},
  pages = {79--109},
  publisher = {Springer International Publishing},
  address = {New York},
  isbn = {978-3-319-77218-9},
  file = {C:\Users\Driver\Dropbox\MPIB\zotero\Driver_Voelkle_2018_Understanding the Time Course of Interventions with Continuous Time Dynamic.pdf}
}

@book{gelman2014bayesian,
  title = {Bayesian Data Analysis},
  author = {Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Rubin, Donald B.},
  year = {2014},
  volume = {2},
  publisher = {Chapman \& Hall/CRC Boca Raton, FL, USA},
  urldate = {2016-08-31},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\heirarchical ct article\\Gelman et al_2014_Bayesian data analysis.pdf;C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\heirarchical ct article\\Gelman et al_2014_Bayesian data analysis2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\83FLFX4M\\01621459.2014.html;C\:\\Users\\Driver\\Zotero\\storage\\XFNTISRW\\01621459.2014.html}
}

@article{guidoum2014pkgsimdiffproc,
  title = {Sim.{{DiffProc}}: Simulation of Diffusion Processes},
  shorttitle = {{{SimDiffProc}}},
  author = {Guidoum, Arsalane Chouaib and Boukhetala, Kamal},
  year = {2014},
  month = sep,
  urldate = {2015-08-28},
  abstract = {The package Sim.DiffProc is an object created in R environment for simulation and modeling of stochastic differential equations (SDE's) the type Ito and Stratonovich. This package contains many objects, the numerical methods to find the solutions to SDE's (1, 2 and 3-dim), with a possibility for simulates a flows trajectories,with good accuracy. Many theoretical problems on the SDE's have become the object of practical research, as statistical analysis and simulation of solution of SDE's, enabled many searchers in different domains to use these equations to modeling and to analyse practical problems, in financial and actuarial modeling and other areas of application, for example modelling and simulate of dispersion in shallow water using the attractive center (Boukhetala K, 1996).  We hope that the package presented here and the updated survey on the subject might be of help for practitioners, postgraduate and PhD students, and researchers in the field who might want to implement new methods.},
  copyright = {GPL ({$\geq$} 3) {\textbar} file LICENCE},
  file = {C:\Users\Driver\Dropbox\MPIB\zotero\ctsem manual\Guidoum_Boukhetala_2014_Sim.pdf}
}

@article{hecht2019bayesian,
  title = {Bayesian Continuous-Time Rasch Models.},
  author = {Hecht, Martin and Hardt, Katinka and Driver, Charles C. and Voelkle, Manuel C.},
  year = {2019},
  journal = {Psychological methods},
  doi = {10.1037/met0000205},
  file = {C\:\\Users\\Driver\\Zotero\\storage\\GF5YVV7S\\sfx_local.html;C\:\\Users\\Driver\\Zotero\\storage\\TLD2R8RG\\2019-22131-001.html}
}

@inproceedings{julierNewExtensionKalman1997,
  title = {A {{New Extension}} of the {{Kalman Filter}} to {{Nonlinear Systems}}},
  author = {Julier, Simon J. and Uhlmann, Jeffrey K.},
  year = {1997},
  pages = {182--193},
  abstract = {The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearises all nonlinear models so that the traditional linear Kalman filter can be applied. Although the EKF (in its many forms) is a widely used filtering strategy, over thirty years of experience with it has led to a general consensus within the tracking and control community that it is difficult to implement, difficult to tune, and only reliable for systems which are almost linear on the time scale of the update intervals. In this paper a new linear estimator is developed and demonstrated. Using the principle that a set of discretely sampled points can be used to parameterise mean and covariance, the estimator yields performance equivalent to the KF for linear systems yet general...},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\methods\\kalman\\Julier_Uhlmann_1997_A New Extension of the Kalman Filter to Nonlinear Systems.pdf;C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\methods\\kalman\\Julier_Uhlmann_1997_A New Extension of the Kalman Filter to Nonlinear Systems2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\N5MFRM5Z\\Julier and Uhlmann - 1997 - New extension of the Kalman filter to nonlinear sy.short;C\:\\Users\\Driver\\Zotero\\storage\\IJ32AXQZ\\summary.html;C\:\\Users\\Driver\\Zotero\\storage\\QHUF6V24\\12.280797.html}
}

@article{king2015pkgpomp,
  title = {Pomp: {{Statistical Inference}} for {{Partially Observed Markov Processes}}},
  shorttitle = {Pomp},
  author = {King, Aaron A. and Ionides, Edward L. and Breto, Carles and Ellner, Stephen P. and Ferrari, Matthew J. and Kendall, Bruce E. and Lavine, Michael and Nguyen, Dao and Reuman, Daniel C. and Wearing, Helen and Wood, Simon N. and Funk, Sebastian},
  year = {2015},
  month = aug,
  urldate = {2015-08-28},
  abstract = {Tools for working with partially observed Markov processes (POMPs, AKA stochastic dynamical systems, state-space models).  'pomp' provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a platform for the implementation of new inference methods.},
  copyright = {GPL-2 {\textbar} GPL-3 [expanded from: GPL ({$\geq$} 2)]}
}

@article{klim2009populationb,
  title = {Population Stochastic Modelling Psm - an {{R}} Package for Mixed-Effects Models Based on Stochastic Differential Equations},
  author = {Klim, S{\o}ren and Mortensen, Stig Bousgaard and Kristensen, Niels Rode and Overgaard, Rune Viig and Madsen, Henrik},
  year = {2009},
  journal = {Computer methods and programs in biomedicine},
  volume = {94},
  number = {3},
  pages = {279--289},
  urldate = {2015-08-28},
  keywords = {Mixed-effect,Pharmacodynamic,Pharmacokinetic,State-space models,Stochastic differential equations (SDEs)},
  file = {C\:\\Users\\Driver\\Zotero\\storage\\3J9VT6WB\\S0169260709000455.html;C\:\\Users\\Driver\\Zotero\\storage\\95C35VMS\\fulltext.html;C\:\\Users\\Driver\\Zotero\\storage\\BTUP2BT2\\S0169260709000455.html}
}

@article{neale2016openmx,
  title = {{{OpenMx}} 2.0: Extended Structural Equation and Statistical Modeling},
  shorttitle = {Openmx 2.0},
  author = {Neale, Michael C. and Hunter, Michael D. and Pritikin, Joshua N. and Zahery, Mahsa and Brick, Timothy R. and Kirkpatrick, Robert M. and Estabrook, Ryne and Bates, Timothy C. and Maes, Hermine H. and Boker, Steven M.},
  year = {2016},
  month = jun,
  journal = {Psychometrika},
  volume = {81},
  number = {2},
  pages = {535--549},
  issn = {1860-0980},
  doi = {10.1007/s11336-014-9435-8},
  urldate = {2023-12-04},
  abstract = {The new software package OpenMx~2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R \$ mechanisms, and improved tab completion from within the R Graphical User Interface.},
  langid = {english},
  keywords = {and Law,Assessment,Assessment Testing and Evaluation,behavior genetics,Behavorial Science,big data,Education,full information maximum likelihood,item factor analysis,latent class analysis,mixture distribution,optimization,ordinal data,path analysis,Path Analysis,Psychometrics,Public Policy,state space modeling,State space modeling,Statistical Theory and Methods,Statistics for Social Science,Statistics for Social Science Behavorial Science Education Public Policy and Law,structural equation modeling,Structural equation modeling,substance use data analysis,Testing and Evaluation,time series},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\crosslagged\\Neale et al_2016_OpenMx 2.pdf;C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\heirarchical ct article\\Neale et al_2016_OpenMx 2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\3I3UJBRT\\Neale et al_2016_OpenMx 2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\68U79X6Z\\Neale et al_2016_OpenMx 2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\6VUDSRNF\\10.html}
}

@article{oud2000continuous,
  title = {Continuous Time State Space Modeling of Panel Data by Means of {{SEM}}.},
  author = {Oud, Johan H. L. and Jansen, Robert A R G},
  year = {2000},
  journal = {Psychometrika},
  volume = {65},
  number = {2},
  pages = {199--215},
  issn = {0033-3123},
  doi = {10.1007/BF02294374},
  abstract = {The end result of a study described here was to improve the Dutch pupil monitoring system LISKAL, which previously only enabled teachers to evaluate decoding and reading comprehension abilities of individual students at two discrete time points each year. A continuous time state space model was developed which enables the teacher to evaluate the pupil's progress continuously as well as at fixed points. Maximum likelihood parameter estimation of the continuous time linear stochastic state space model is considered on the basis of large N discrete time data using a structural equation modeling (SEM) program. Random subject effects are allowed to be part of the model. The exact discrete model (EDM) is employed which links the discrete time model parameters to the underlying continuous time model parameters by means of nonlinear restrictions. The EDM is generalized to cover not only time-invariant parameters but also the cases of stepwise time-varying (piecewise time-invariant) parameters and parameters varying continuously over time according to a general polynomial scheme. The identification of the continuous time parameters is discussed and an educational example (with 638 Dutch first and second graders) is presented. (PsycINFO Database Record (c) 2012 APA, all rights reserved)},
  keywords = {1st & 2nd grade students,and Law,Assessment,Assessment Testing and Evaluation,Behavorial Science,Cognitive Processes,continuous time state space & exact discrete mod,continuous time state space modeling,Education,Educational Measurement,Elementary School Students,exact discrete model,linear stochastic differential equations,longitudinal structural equation modeling,Mathematical Modeling,PANEL analysis,Psychometrics,Public Policy,Reading Comprehension,Reading Measures,Statistical Theory and Methods,Statistics for Social Science,Statistics for Social Science Behavorial Science Education Public Policy and Law,Testing and Evaluation},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\interventions_article\\Oud_Jansen_2000_Continuous time state space modeling of panel data by means of SEM.pdf;C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\interventions_article\\Oud_Jansen_2000_Continuous time state space modeling of panel data by means of SEM2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\8T23VTEZ\\BF02294374.html;C\:\\Users\\Driver\\Zotero\\storage\\HET59X2R\\BF02294374.html}
}

@article{oud2008continuous,
  title = {Continuous Time Modeling of Panel Data: {{SEM}} versus Filter Techniques},
  shorttitle = {Continuous {{Time Modeling}} of {{Panel Data}}},
  author = {Oud, Johan H. L. and Singer, Hermann},
  year = {2008},
  journal = {Statistica Neerlandica},
  volume = {62},
  number = {1},
  pages = {4--28},
  issn = {00390402},
  doi = {10.1111/j.1467-9574.2007.00376.x},
  abstract = {TeX output 2008.01.10:1357},
  keywords = {damped linear oscillator,Damped linear oscillator,exact discrete model,Exact discrete model,Kalman filter,random subject effects,Random subject effects,state-space model,State-space model,structural equation modeling,Structural equation modeling},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\methods\\continuous\\Oud_Singer_2008_Continuous time modeling of panel data.pdf;C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\methods\\continuous\\Oud_Singer_2008_Continuous time modeling of panel data2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\CN66TRHJ\\full.html;C\:\\Users\\Driver\\Zotero\\storage\\SPR4VMIN\\full.html}
}

@article{oud2018sem,
  title = {{{SEM}} Based {{CARMA}} Time Series Models for Arbitrary {{N}}},
  author = {Oud, Johan H. L. and Voelkle, Manuel C. and Driver, Charles C.},
  year = {2018},
  journal = {Multivariate Behavioral Research},
  volume = {53},
  pages = {36--56},
  doi = {10.1080/00273171.2017.1383224}
}

@book{rcoreteam2014r,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  year = {2014},
  publisher = {R Foundation for Statistical Computing},
  address = {Vienna, Austria}
}

@article{redhead2019dynamics,
  title = {On the Dynamics of Social Hierarchy: A Longitudinal Investigation of the Rise and Fall of Prestige, Dominance, and Social Rank in Naturalistic Task Groups},
  author = {Redhead, Daniel and Cheng, Joey T. and Driver, Charles and Foulsham, Tom and O'Gorman, Rick},
  year = {2019},
  month = mar,
  journal = {Evolution and Human Behavior},
  volume = {40},
  number = {2},
  pages = {222--234},
  publisher = {Elsevier BV},
  doi = {10.1016/j.evolhumbehav.2018.12.001}
}

@article{singer1993continuoustime,
  title = {Continuous-Time Dynamical Systems with Sampled Data, Errors of Measurement and Unobserved Components},
  author = {Singer, Hermann},
  year = {1993},
  month = sep,
  journal = {Journal of Time Series Analysis},
  volume = {14},
  number = {5},
  pages = {527--545},
  issn = {1467-9892},
  doi = {10.1111/j.1467-9892.1993.tb00162.x},
  urldate = {2014-07-30},
  abstract = {Abstract.  Maximum likelihood estimation of sampled continuous-time stochastic processes is considered. The likelihood is directly maximized with respect to the original structural parameters using a scoring algorithm with exact analytical derivatives. Furthermore, the case of unobserved states and errors of measurement is treated via EM and quasi-Newton algorithms. The proposed methods are illustrated with simulation studies and analysis of sunspot activity.},
  langid = {english},
  keywords = {analytical derivatives,continuous/discrete state space model:discrete measurements,EM,maximum likelihood estimation,scoring,Stochastic differential equations},
  annotation = {00046},
  file = {C\:\\Users\\Driver\\Zotero\\storage\\FIHEI97C\\Singer - 1993 - Continuous-time dynamical systems with sampled dat;C\:\\Users\\Driver\\Zotero\\storage\\C7IN5JQV\\abstract.html}
}

@misc{standevelopmentteam_rstan,
  title = {{{RStan}}: The {{R}} Interface to {{Stan}}},
  shorttitle = {{{RStan}}},
  author = {{Stan Development Team}}
}

@article{tomasson2013computational,
  title = {Some Computational Aspects of {{Gaussian CARMA}} Modelling},
  author = {T{\'o}masson, Helgi},
  year = {2013},
  month = nov,
  journal = {Statistics and Computing},
  volume = {25},
  number = {2},
  pages = {375--387},
  issn = {0960-3174, 1573-1375},
  doi = {10.1007/s11222-013-9438-9},
  urldate = {2016-08-29},
  abstract = {Representation of continuous-time ARMA (Auto-Regressive-Moving-Average), CARMA, time-series models is reviewed. Computational aspects of simulating and calculating the likelihood-function of CARMA models are summarized. Some numerical properties are illustrated by simulations. Methods for enforcing the stationarity restriction on the parameter space are discussed. Due to such methods restricted numerical estimation enforcing stationarity is possible. The impact of scaling of time axis on the magnitude of the parameters is demonstrated. Proper scaling of the time axis can give parameter values of similar magnitude which is useful for numerical work. The practicality of the computational approach is illustrated with some real and simulated data.},
  langid = {english},
  keywords = {CARMA,Continuous time,Maximum-likelihood,Simulation,Spectrum,Time-series},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\heirarchical ct article\\reading_general\\Tomasson_2013_Some computational aspects of Gaussian CARMA modelling.pdf;C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\heirarchical ct article\\reading_general\\Tomasson_2013_Some computational aspects of Gaussian CARMA modelling2.pdf;C\:\\Users\\Driver\\Zotero\\storage\\DCGUPSAQ\\s11222-013-9438-9.html;C\:\\Users\\Driver\\Zotero\\storage\\RR99ZJF3\\fulltext.html}
}

@article{voelkle2013continuous,
  title = {Continuous Time Modelling with Individually Varying Time Intervals for Oscillating and Non-Oscillating Processes},
  author = {Voelkle, Manuel C. and Oud, Johan H. L.},
  year = {2013},
  journal = {British Journal of Mathematical and Statistical Psychology},
  volume = {66},
  number = {1},
  pages = {103--126},
  issn = {2044-8317},
  doi = {10.1111/j.2044-8317.2012.02043.x},
  urldate = {2014-01-27},
  abstract = {When designing longitudinal studies, researchers often aim at equal intervals. In practice, however, this goal is hardly ever met, with different time intervals between assessment waves and different time intervals between individuals being more the rule than the exception. One of the reasons for the introduction of continuous time models by means of structural equation modelling has been to deal with irregularly spaced assessment waves (e.g., Oud \& Delsing, 2010). In the present paper we extend the approach to individually varying time intervals for oscillating and non-oscillating processes. In addition, we show not only that equal intervals are unnecessary but also that it can be advantageous to use unequal sampling intervals, in particular when the sampling rate is low. Two examples are provided to support our arguments. In the first example we compare a continuous time model of a bivariate coupled process with varying time intervals to a standard discrete time model to illustrate the importance of accounting for the exact time intervals. In the second example the effect of different sampling intervals on estimating a damped linear oscillator is investigated by means of a Monte Carlo simulation. We conclude that it is important to account for individually varying time intervals, and encourage researchers to conceive of longitudinal studies with different time intervals within and between individuals as an opportunity rather than a problem.},
  langid = {english},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\methods\\continuous\\Voelkle_Oud_2013_Continuous time modelling with individually varying time intervals for.pdf;C\:\\Users\\Driver\\Zotero\\storage\\83XSWD8G\\full.html;C\:\\Users\\Driver\\Zotero\\storage\\Q7XR2KN7\\full.html}
}

@article{voelkle2015relating,
  title = {Relating Latent Change Score and Continuous Time Models},
  author = {Voelkle, Manuel C and Oud, Johan HL},
  year = {2015},
  journal = {Structural Equation Modeling: A Multidisciplinary Journal},
  volume = {22},
  number = {3},
  pages = {366--381},
  publisher = {Taylor \& Francis},
  doi = {10.1080/10705511.2014.935918},
  keywords = {continuous time models,difference equation,differential equation,latent change score models,time intervals},
  file = {C\:\\Users\\Driver\\Dropbox\\MPIB\\zotero\\crosslagged\\Voelkle_Oud_2015_Relating Latent Change Score and Continuous Time Models.pdf;C\:\\Users\\Driver\\Zotero\\storage\\G2T6NCAK\\10705511.2014.html}
}

@article{voelkle2019role,
  title = {The Role of Time in the Quest for Understanding Psychological Mechanisms},
  author = {Voelkle, Manuel C. and Gische, Christian and Driver, Charles C. and Lindenberger, Ulman},
  year = {2019},
  month = jan,
  journal = {Multivariate Behavioral Research},
  volume = {53},
  number = {6},
  pages = {1--24},
  issn = {0027-3171, 1532-7906},
  doi = {10.1080/00273171.2018.1496813},
  urldate = {2019-03-28},
  langid = {english}
}

@book{xie2015dynamic,
  title = {Dynamic {{Documents}} with {{R}} and {{Knitr}}},
  author = {Xie, Yihui},
  year = {2015},
  edition = {2nd},
  publisher = {{Chapman and Hall/CRC}},
  address = {Boca Raton, Florida},
  doi = {10.1201/b15166}
}
