**Dynamic Graphical Models** (**DGM**)

Dynamic graphical models for multivariate time series data to estimate directed dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et al. (2017) <doi:10.1101/198887>.

**Utility Functions of the Potts Models** (**PottsUtils**)

There are three sets of functions. The first produces basic properties of a graph and generates samples from multinomial distributions to facilitate the simulation functions (they maybe used for other purposes as well). The second provides various simulation functions for a Potts model in Potts, R. B. (1952) <doi:10.1017/S0305004100027419>. The third currently includes only one function which computes the normalizing constant of a Potts model based on simulation results.

**Joint Analysis of Experiments with Mixtures and Random Effects** (**Blendstat**)

Package to perform a joint analysis of experiments with mixtures and random effects, assuming a process variable, represented by a covariate, Kalirajan K P (1990) <doi:10.1080/757582835>.

**SPatially aUTomatic deNoising for Ims toolKit** (**SPUTNIK**)

A set of tools for the peak filtering of mass spectrometry imaging data (MSI or IMS) based on spatial distribution of signal. Given a region-of-interest (ROI), representing the spatial region where the informative signal is expected to be localized, a series of filters determine which peak signals are characterized by an implausible spatial distribution. The filters reduce the dataset dimensionality and increase its information vs noise ratio, improving the quality of the unsupervised analysis results, reducing data dimensionality and simplifying the chemical interpretation.

**Bootstrapped Robustness Assessment for Qualitative Comparative Analysis** (**braQCA**)

Test the robustness of a user’s Qualitative Comparative Analysis solutions to randomness, using the bootstrapped assessment: baQCA(). This package also includes a function that provides recommendations for improving solutions to reach typical significance levels: brQCA(). After applying recommendations from brQCA(), QCAdiff() shows which cases are excluded from the final result.

### Like this:

Like Loading...

*Related*