Showing 6 of total 6 results (show query)
kingaa
pomp:Statistical Inference for Partially Observed Markov Processes
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Maintained by Aaron A. King. Last updated 9 days ago.
abcb-splinedifferential-equationsdynamical-systemsiterated-filteringlikelihoodlikelihood-freemarkov-chain-monte-carlomarkov-modelmathematical-modellingmeasurement-errorparticle-filtersequential-monte-carlosimulation-based-inferencesobol-sequencestate-spacestatistical-inferencestochastic-processestime-seriesopenblas
114 stars 11.74 score 1.3k scripts 4 dependents
stewid
SimInf:A Framework for Data-Driven Stochastic Disease Spread Simulations
Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172>.
Maintained by Stefan Widgren. Last updated 18 days ago.
data-drivenepidemiologyhigh-performance-computingmarkov-chainmathematical-modellinggslopenmp
35 stars 10.09 score 227 scripts
bioc
wateRmelon:Illumina DNA methylation array normalization and metrics
15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages.
Maintained by Leo C Schalkwyk. Last updated 4 months ago.
dnamethylationmicroarraytwochannelpreprocessingqualitycontrol
7.75 score 247 scripts 2 dependents
jeswheel
panelPomp:Inference for Panel Partially Observed Markov Processes
Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.
Maintained by Jesse Wheeler. Last updated 4 months ago.
5.91 score 45 scriptsbioc
bigmelon:Illumina methylation array analysis for large experiments
Methods for working with Illumina arrays using gdsfmt.
Maintained by Leonard C. Schalkwyk. Last updated 5 months ago.
dnamethylationmicroarraytwochannelpreprocessingqualitycontrolmethylationarraydataimportcpgisland
5.47 score 21 scripts
cran
TSSS:Time Series Analysis with State Space Model
Functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0).
Maintained by Masami Saga. Last updated 2 years ago.
2 stars 1.78 score