Browse Items (8396 total)

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…

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…

Modeling complex real-world situations such as infectious diseases, geological phenomena, and biological processes can present a dilemma: the computer model (referred to as
a simulator) needs to be complex enough to capture the dynamics of the…

Hidden Markov models constitute a versatile class of statistical models for time series
that are driven by hidden states. In financial applications, the hidden states can often
be linked to market regimes such as bearish and bullish markets or…

The hyper2 package provides functionality to work with extensions of the BradleyTerry probability model such as Plackett-Luce likelihood including team strengths and
reified entities (monsters). The package allows one to use relatively natural R…

Changepoint detection is an important problem with a wide range of applications.
There are many different types of changes that one may wish to detect, and a wide
range of algorithms and software for detecting them. However there are relatively…

The Extremes.jl package provides exhaustive, high-performance functions by leveraging the multiple-dispatch capabilities in Julia for the analysis of extreme values. In
particular, the package implements statistical models for both block maxima and…

This article introduces funGp, an R package which handles regression problems involving multiple scalar and/or functional inputs, and a scalar output, through the Gaussian
process model. This is particularly of interest for the design and analysis…