close

Showing 5 of total 5 results (show query)

inlabru-org

inlabru:Bayesian Latent Gaussian Modelling using INLA and Extensions

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

Maintained by Finn Lindgren. Last updated 11 hours ago.

96 stars 12.60 score 832 scripts 6 dependents

demsarjure

autohrf:Automated Generation of Data-Informed GLM Models in Task-Based fMRI Data Analysis

Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner.

Maintained by Jure Demšar. Last updated 1 years ago.

2 stars 4.72 score 13 scripts

epiforecasts

EpiSoon:Forecast Cases Using Reproduction Numbers

To forecast the time-varying reproduction number and use this to forecast reported case counts. Includes tools to evaluate a range of models across samples and time series using proper scoring rules.

Maintained by Sam Abbott. Last updated 2 years ago.

case-forecastsforecasts

7 stars 4.26 score 25 scripts 1 dependents

vasileioskarapoulios

LDNN:Longitudinal Data Neural Network

This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.

Maintained by Vasileios Karapoulios. Last updated 4 years ago.

3.70 score 6 scripts

philipp-baumann

simplerspec:Soil and plant spectroscopic model building and prediction

Functions that cover reading of spectral data, outlier removal, spectral preprocessing, calibration sampling, PLS regression using caret, and model diagnostic statistics and plots.

Maintained by Philipp Baumann. Last updated 2 years ago.

33 stars 3.52 score 10 scripts

: