At the faculty BSS, I am employed on the chair Applied Statistics and Data Visualisation. 
My research focuses on the development of statistical models for applications in the social sciences. These models often contain dynamic or longitudinal components. Furthermore, I’m interested in science communication - for which data visualisation plays a key role - and the improvement of scientific practice in general.

Furthermore, I'm Director of Research of the Heymans Institute for Psychological Research.

See casperalbers.nl for details.

Biostatistics. Latent Class modeling.
Multilevel. Longitudinal.
R.

(Application of medical) statistics in general, with special interests in causality (DAGs and applications with observational data), causal  inference search algorithms and mediation analysis.  (Generalized) linear mixed effects models, longitudinal data analyses, growth curves analyses (GAMLSS).

econometrics, instrumental variables, many instruments asymptotics, exact inference, measurement errors, identification, quantitative finance, term structure of interest rates, generalized-dynamic arbitrage-free yield models

Data Science

Statistics; Causal Inference; Machine Learning; (Pharmaco)Epidemiology; g-formula.

statistics, social network analysis, multilevel analysis

One of my main research themes involves analyzing intensive longitudinal data. Particularly, I am interested in formulating statistical models that describe the between-individual differences that are present in such data. I often employ and develop clustering models for intensive longitudinal data to identify more homogeneous subgroups of people.

Another core interests lies in the implementation of novel statistical methodology into freely available software. The code to estimate the statistical models that are discussed in my research projects can be found on the Open Science Framework or GitHub (links can be found below).

I also research how statistical models are used and interpreted by empirical researchers.

My research group works on the identification of molecular effects that disease-causing mutations have. More information on my work can be found on http://www.ludesign.nl/frankelab

Statistics

Bayesian Statistics, Computational Statistics, Bayesian networks

NWO-VENI laureate Prof. Hak and his multidisciplinary team have supervised >30 PhD theses on the validity of scientific statistical and epidemiological causal design methods to assess (side-) effects of drugs and vaccines using Real-World Big Health Care and Biobank Data. Scientific methods consider a variety of designs such as case-control, cohort, case-only, sibling designs, instrumental variable analysis, propensity score analysis often in combination with RCTs, meta-analyses and Individual Patient Data designs. His scientific work led to >400 peer-reviewed publications till 2021 (Web of Science H-index 44, >8,000 citations). As leader of the PharmLines Initiative,  a public-private collaboration between Lifelines BV-UMCG-RUG, he initiates and co-supervises research in the area of Precision Medicine with linked biobank, genetic, clinical, pharmacological and health care data of >80,000 persons from the Northern region. Topics mainly concern the pharmaco-epidemiology of preventive medications as well as vaccinations. In addition, his team supports the societal impact by studying the optimal methods to implement innovations in the area of sustainable drug applications and  vaccination strategies (e.g. drug repurposing, data science technologies to support optimal drug use, development of infection risk profiles and vaccination passport).

Prof. Hak is program director of the two-year Groningen Research Master Medical Pharmaceutical Sciences. As a director he implemented innovations regarding the extension of research tracks including Pharmaceutical Design & Engineering, Drug Toxicology &Translational Technologies and Pharmacoepidemiology & -Economics. With these tracks the complete drug development cycle can be studied within MPS. He further introduced a strong mentorate for students, monitoring of research projects, clear selection criteria for examiners and committee evaluations of research projects, which resulted in a Top-label in 2017 and 2019 by the influential Dutch Keuzegids (MPS belongs to the top-10% best master educations in the Netherlands). As educational director Pharmacy (>100 faculty) he applies his broad expertise to lead the cluster Pharmacy, also in close collaboration with sister Faculties.

statistical modeling and analysis of networks in materials science;
topological data analysis;
random networks in statistical physics;
interplay between dynamical systems and probabilit

Statistics, missing data, multiple imputation, social network analysis

Data analysis and statistics, with a special interest in methods for searching underlying structures in large two-way and three-way data set. More info, including publication list, and suggestion to ask sending full papers: http://kiers.webhosting.rug.nl

Applied microeconometrics, Insurance economics, Non-life insurance models, Economics and econometrics of sports

Statistics, test theory, IRT

Bayesian statistics
Statistical genetics
Functional Genomics
Machine Learning
Evolutionary biology

Health economics 
Epidemiology

Survey Research Methodology, Interviewer-respondent interaction in Survey interviews, Sequence Viewer

inter-rater reliability, missing data, multiple imputation

Bayesian statistics, computational methods, reproducibility

For an overview of my research and projects, please see my personal website: http://romeijn.web.rug.nl.

Growth mixture modeling, Latent trajectory analysis, Latent class analysis, measurement invariance

Nutrition and Outcome after Bariatric Surgery: focus on Weight Reduction, Muscle and Metabolism.

social networks, multilevel analysis, stochastic models in the social and behavioral sciences, statistics

social networks, stochastic modelling

Multilevel/Linear Structural Equation Modeling LISREL/SEM; Latent Class Analysis (LCA)

My main research theme involves explaining observed data, typically resulting from psychological research, with a meaningful and concise model. This task becomes really challenging when multivariate and/or longitudinal measurements are to be modeled jointly. Specific methods of interests concern the development and evaluation of 1. data reduction modeling (e.g., principal component analysis); 2. latent variable modeling (e.g., exploratory factor analysis); 3. cluster modeling (e.g., mixture analysis); 4. modeling for test construction (e.g., GAMLSS model for test norming).

electroencephalography, eyetracking, statistics, experimental design, teaching

Stochastic models for the spread of infectious diseases

Nutrition & Asthma

My main research interests lie in the development of patient-centered diagnostic and risk-prediction models for use in psychiatric practice. More specifically, my research focuses on the methodological and statistical approaches that are needed to develop such models in evidence-based research. My methodological/statistical interests lie in (1) Modern Psychometrics, (2) Modeling of (intensive) longitudinal data, (3) Prediction Modeling and (4) Machine Learning.
As part of my job, I work as a statistical advisor for researchers in the UMCG Department of Psychiatry and in affiliated mental healthcare institutions.

Data Science, Marketing Analytics, Econometrics, Marketing Model Building, Time series analysis, State Space Modeling, Kalman Filtering, Diffusion modelling, Database Marketing, Privacy, Pharmaceutical marketing, Statistical Quality Control, Six Sigma

Network modelling; High-dimensional inference; inference of ODEs; Statistical genomics

Bioinformatics, biostatistics

Mathematical statistics

Risk theory, Actuarial sciences, Risk insurance, Probability theory, Mathematics,
Fractional calculus, Financial Mathematics