126 citations · 455 across the 18 of their papers we have counts for
18 papers
10 quick tips for making your software outlive your job
Richard Littauer, Greg Wilson, Jan Ainali +19
Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, f…
Feedback-augmented Non-homogeneous Hidden Markov Models for Longitudinal Causal Inference
Jouni Helske
Hidden Markov models are widely used for modeling sequential data but typically have limited applicability in observational causal inference due to their strong conditional indepen…
Hidden Markov modelling of spatio-temporal dynamics of measles in 1750-1850 Finland
Tiia-Maria Pasanen, Jouni Helske, Tarmo Ketola
Real world spatio-temporal datasets, and phenomena related to them, are often challenging to visualise or gain a general overview of. In order to summarise information encompassed…
Spatio-temporal modeling of co-dynamics of smallpox, measles and pertussis in pre-healthcare Finland
Tiia-Maria Pasanen, Jouni Helske, Harri Högmander +1
Infections are known to interact as previous infections may have an effect on risk of succumbing to a new infection. The co-dynamics can be mediated by immunosuppression or -modula…
A modern approach to transition analysis and process mining with Markov models: A tutorial with R
Jouni Helske, Satu Helske, Mohammed Saqr +2
This chapter presents an introduction to Markovian modeling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapter…
Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach
Lauri Valkonen, Santtu Tikka, Jouni Helske +1
Pricing decisions of companies require an understanding of the causal effect of a price change on the demand. When real-life pricing experiments are infeasible, data-driven decisio…
dynamite: An R Package for Dynamic Multivariate Panel Models
Santtu Tikka, Jouni Helske
dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising multiple measurements per multiple individuals measured in time. The package suppor…
Clustering and Structural Robustness in Causal Diagrams
Santtu Tikka, Jouni Helske, Juha Karvanen
Graphs are commonly used to represent and visualize causal relations. For a small number of variables, this approach provides a succinct and clear view of the scenario at hand. As…
A Bayesian spatio-temporal analysis of markets during the Finnish 1860s famine
Tiia-Maria Pasanen, Miikka Voutilainen, Jouni Helske +1
We develop a Bayesian spatio-temporal model to study pre-industrial grain market integration during the Finnish famine of the 1860s. Our model takes into account several problemati…
bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
Jouni Helske, Matti Vihola
We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Ga…
Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R
Jouni Helske
The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model…
Estimation of causal effects with small data in the presence of trapdoor variables
Jouni Helske, Santtu Tikka, Juha Karvanen
We consider the problem of estimating causal effects of interventions from observational data when well-known back-door and front-door adjustments are not applicable. We show that…
Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect
Jouni Helske, Satu Helske, Matthew Cooper +2
Common reporting styles for statistical results in scientific articles, such as p-values and confidence intervals (CI), have been reported to be prone to dichotomous interpretation…
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten, Jouni Helske, Matti Vihola
Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and ra…
Introducing libeemd: A program package for performing the ensemble empirical mode decomposition
P. J. J. Luukko, J. Helske, E. Räsänen
The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode d…
Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
Satu Helske, Jouni Helske
Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to descr…
KFAS: Exponential Family State Space Models in R
Jouni Helske
State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes an R package KFAS for state…
Importance sampling type estimators based on approximate marginal MCMC
Matti Vihola, Jouni Helske, Jordan Franks
We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context…