Browse Items (8 total)

When predicting future events, it is common to issue forecasts that are probabilistic,
in the form of probability distributions over the range of possible outcomes. Such forecasts
can be evaluated using proper scoring rules. Proper scoring rules…

Multivariate spatio-temporal data refers to multiple measurements taken across space
and time. For many analyses, spatial and time components can be separately studied: for
example, to explore the temporal trend of one variable for a single spatial…

The sparse group lasso is a high-dimensional regression technique that is useful for
problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we…

The bayesnec package has been developed for R to fit concentration (dose)-response
curves (CR) to toxicity data for the purpose of deriving no-effect-concentration (NEC), nosignificant-effect-concentration (NSEC), and effect-concentration (of…

Machine learning algorithms are useful for various prediction tasks, but they can also
learn how to discriminate, based on gender, race or other sensitive attributes. This realization gave rise to the field of fair machine learning, which aims to…

Priors allow us to robustify inference and to incorporate expert knowledge in Bayesian
hierarchical models. This is particularly important when there are random effects that
are hard to identify based on observed data. The challenge lies in…

Performing inference in statistical models with an intractable likelihood is challenging,
therefore, most likelihood-free inference (LFI) methods encounter accuracy and efficiency
limitations. In this paper, we present the implementation of the LFI…

One of the contemporary challenges in anomaly detection is the ability to detect,
and differentiate between, both point and collective anomalies within a data sequence or
time series. The anomaly package has been developed to provide users with a…