R-universe search: "ornstein-uhlenbeck"

kingaa

ouch:Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses

Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.

Maintained by Aaron A. King. Last updated 4 months ago.

adaptive-regime brownian-motion ornstein-uhlenbeck ornstein-uhlenbeck-models ouch phylogenetic-comparative-hypotheses phylogenetic-comparative-methods phylogenetic-data react

42.4 match 15 stars 6.87 score 68 scripts 4 dependents

jclavel

mvMORPH:Multivariate Comparative Tools for Fitting Evolutionary Models to Morphometric Data

Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.

Maintained by Julien Clavel. Last updated 1 months ago.

openblas

14.8 match 17 stars 9.46 score 189 scripts 3 dependents

bioc

scoup:Simulate Codons with Darwinian Selection Modelled as an OU Process

An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model is built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enables realistic evolutionary process of living organisms to be reproducible seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and fitness landscape fluctuations on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and stochastic fitness landscape update techniques are available.

Maintained by Hassan Sadiq. Last updated 2 months ago.

alignment classification comparativegenomics dataimport genetics mathematicalbiology researchfield sequencing sequencematching software statisticalmethod workflowstep

19.9 match 4.60 score 8 scripts

venelin

POUMM:The Phylogenetic Ornstein-Uhlenbeck Mixed Model

The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the phylogenetic heritability of continuous traits, to test hypotheses of neutral evolution versus stabilizing selection, to quantify the strength of stabilizing selection, to estimate measurement error and to make predictions about the evolution of a phenotype and phenotypic variation in a population. The package implements combined maximum likelihood and Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood calculation, maximum likelihood inference of the genotypic values at the tips, functions for summarizing and plotting traces and posterior samples, functions for simulation of a univariate continuous trait evolution model along a phylogenetic tree. So far, the package has been used for estimating the heritability of quantitative traits in macroevolutionary and epidemiological studies, see e.g. Bertels et al. (2017) <doi:10.1093/molbev/msx246> and Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for parallel POUMM likelihood calculation has been published in Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.

Maintained by Venelin Mitov. Last updated 4 years ago.

cpp

17.6 match 4 stars 4.94 score 22 scripts

kopperud

slouch:Stochastic Linear Ornstein-Uhlenbeck Comparative Hypotheses

An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.

Maintained by Bjørn Tore Kopperud. Last updated 1 years ago.

13.7 match 2 stars 5.12 score 44 scripts 1 dependents

klvoje

evoTS:Analyses of Evolutionary Time-Series

Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the 'paleoTS' package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.

Maintained by Kjetil Lysne Voje. Last updated 9 months ago.

16.5 match 1 stars 4.26 score 184 scripts

emmanuelparadis

ape:Analyses of Phylogenetics and Evolution

Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.

Maintained by Emmanuel Paradis. Last updated 15 hours ago.

openblas cpp

3.6 match 64 stars 17.22 score 13k scripts 599 dependents

squidlobster

castor:Efficient Phylogenetics on Large Trees

Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.

Maintained by Stilianos Louca. Last updated 4 months ago.

cpp

10.2 match 2 stars 5.75 score 450 scripts 9 dependents

siacus

sde:Simulation and Inference for Stochastic Differential Equations

Companion package to the book Simulation and Inference for Stochastic Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY. *

Maintained by Stefano Maria Iacus. Last updated 2 years ago.

7.0 match 7.02 score 178 scripts 15 dependents

acguidoum

Sim.DiffProc:Simulation of Diffusion Processes

It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.

Maintained by Arsalane Chouaib Guidoum. Last updated 1 years ago.

dynamic-system moment-equations monte-carlo-simulation parallel-computing stochastic-calculus stochastic-differential-equation transition-density

6.0 match 13 stars 7.69 score 86 scripts 4 dependents

mindthegap-erc

StratPal:Stratigraphic Paleobiology Modeling Pipelines

The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.

Maintained by Niklas Hohmann. Last updated 25 days ago.

palaeobiology palaeontology paleobiology paleontology stratigraphic-paleobiology stratigraphy

7.0 match 1 stars 5.88 score 18 scripts

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 1 months ago.

abc b-spline differential-equations dynamical-systems iterated-filtering likelihood likelihood-free markov-chain-monte-carlo markov-model mathematical-modelling measurement-error particle-filter sequential-monte-carlo simulation-based-inference sobol-sequence state-space statistical-inference stochastic-processes time-series openblas

3.4 match 115 stars 11.81 score 1.3k scripts 4 dependents

moviedo5

fda.usc:Functional Data Analysis and Utilities for Statistical Computing

Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.

Maintained by Manuel Oviedo de la Fuente. Last updated 4 months ago.

functional-data-analysis fortran

4.0 match 12 stars 9.72 score 560 scripts 22 dependents

phillipbvetter

ctsmTMB:Continuous Time Stochastic Modelling using Template Model Builder

ctsmTMB is an R package for parameter and state estimation and forecasting in stochastic state space systems. These are partially observed state space systems that whose latent states evolve according to a system of stochastic differential equation. The package employs the R6 object for easy model construction, and uses the TMB/RTMB packages for its modern C++ implementations and algorithmic differentiation. The estimation methods implemented are variations of (non-linear) Kalman Filters in addition to the Laplace approximation.

Maintained by Phillip Brinck Vetter. Last updated 6 days ago.

cpp

7.5 match 2 stars 5.10 score

lamho86

phylolm:Phylogenetic Linear Regression

Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree.

Maintained by Lam Si Tung Ho. Last updated 4 months ago.

3.4 match 33 stars 10.79 score 318 scripts 14 dependents

thej022214

OUwie:Analysis of Evolutionary Rates in an OU Framework

Estimates rates for continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al (2012).

Maintained by Jeremy Beaulieu. Last updated 1 months ago.

4.4 match 9 stars 8.37 score 161 scripts

puttickmacroevolution

motmot:Models of Trait Macroevolution on Trees

Functions for fitting models of trait evolution on phylogenies for continuous traits. The majority of functions described in Thomas and Freckleton (2012) <doi:10.1111/j.2041-210X.2011.00132.x> and include functions that allow for tests of variation in the rates of trait evolution.

Maintained by Mark Puttick. Last updated 5 years ago.

cpp

6.0 match 4 stars 6.05 score 35 scripts

cran

paleoTS:Analyze Paleontological Time-Series

Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).

Maintained by Gene Hunt. Last updated 6 months ago.

8.1 match 1 stars 4.48 score 2 dependents

hckiang

glinvci:Phylogenetic Comparative Methods with Uncertainty Estimates

A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.

Maintained by Woodrow Kiang. Last updated 11 months ago.

fortran openblas openmp

8.0 match 4.00 score 5 scripts

clementcalenge

adehabitatLT:Analysis of Animal Movements

A collection of tools for the analysis of animal movements.

Maintained by Clement Calenge. Last updated 6 months ago.

3.6 match 6 stars 8.60 score 370 scripts 12 dependents

mlysy

msde:Bayesian Inference for Multivariate Stochastic Differential Equations

Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components. The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.

Maintained by Martin Lysy. Last updated 3 years ago.

cpp

6.1 match 4.91 score 27 scripts

cran

mvSLOUCH:Multivariate Stochastic Linear Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses

Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) <doi:10.1016/j.jtbi.2012.08.005>. The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from <https://github.com/venelin/PCMBaseCpp/>.

Maintained by Krzysztof Bartoszek. Last updated 1 years ago.

9.8 match 3.00 score 1 dependents

epiforecasts

EpiNow2:Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters

Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported.

Maintained by Sebastian Funk. Last updated 25 days ago.

backcalculation covid-19 gaussian-processes open-source reproduction-number stan cpp

2.3 match 120 stars 11.88 score 210 scripts

andy-iskauskas

hmer:History Matching and Emulation Package

A set of objects and functions for Bayes Linear emulation and history matching. Core functionality includes automated training of emulators to data, diagnostic functions to ensure suitability, and a variety of proposal methods for generating 'waves' of points. For details on the mathematical background, there are many papers available on the topic (see references attached to function help files or the below references); for details of the functions in this package, consult the manual or help files. Iskauskas, A, et al. (2024) <doi:10.18637/jss.v109.i10>. Bower, R.G., Goldstein, M., and Vernon, I. (2010) <doi:10.1214/10-BA524>. Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) <doi:10.1007/978-1-4612-2290-3_2>.

Maintained by Andrew Iskauskas. Last updated 12 days ago.

3.8 match 16 stars 7.19 score 37 scripts

venelin

PCMBase:Simulation and Likelihood Calculation of Phylogenetic Comparative Models

Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.

Maintained by Venelin Mitov. Last updated 10 months ago.

3.4 match 6 stars 7.56 score 85 scripts 3 dependents

egarpor

goffda:Goodness-of-Fit Tests for Functional Data

Implementation of several goodness-of-fit tests for functional data. Currently, mostly related with the functional linear model with functional/scalar response and functional/scalar predictor. The package allows for the replication of the data applications considered in García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga (2021) <doi:10.1111/sjos.12486>.

Maintained by Eduardo García-Portugués. Last updated 1 years ago.

functional-data-analysis goodness-of-fit reproducible-research statistics openblas cpp

3.8 match 10 stars 4.76 score 19 scripts 1 dependents

egarpor

sdetorus:Statistical Tools for Toroidal Diffusions

Implementation of statistical methods for the estimation of toroidal diffusions. Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the replicability of the results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.

Maintained by Eduardo García-Portugués. Last updated 1 years ago.

circular-statistics inference maximum-likelihood reproducible-research sde statistics toroidal-data openblas cpp

4.4 match 5 stars 3.88 score 9 scripts 1 dependents

tomaspinall

LSMRealOptions:Value American and Real Options Through LSM Simulation

The least-squares Monte Carlo (LSM) simulation method is a popular method for the approximation of the value of early and multiple exercise options. 'LSMRealOptions' provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. 'LSMRealOptions' values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. 'LSMRealOptions' provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) <doi:10.1093/rfs/14.1.113>.

Maintained by Thomas Aspinall. Last updated 4 years ago.

3.3 match 4.26 score 12 scripts 1 dependents

cran

TOU:Transformed Ornstein-Uhlenbeck Model for Adsorption Kinetics

Estimates the parameters of a Transformed Ornstein-Uhlenbeck (TOU) stochastic model for adsorption data and also the parameters of the related pseudo-n-order (PNO) model, such as the maximum adsorption capacity (qe), the adsorption rate constant (kn) and the order of the model (n).

Maintained by Rogelio Salinas-Gutiérrez. Last updated 2 years ago.

13.7 match 1.00 score

calbertsen

argosTrack:Fit Movement Models to Argos Data for Marine Animals

Fits various state-space animal movement models to Argos data. Models are fitted by maximum likelihood with the Laplace approximation via 'TMB' and 'nlminb'. Functionality to visualize and simulate the models is available.

Maintained by Christoffer Moesgaard Albertsen. Last updated 4 years ago.

animal animal-behavior argos-data c-plus-plus gps-data movement movement-ecology solar-geolocator tracking cpp

3.4 match 12 stars 3.92 score 14 scripts

peterreichert

timedeppar:Infer Constant and Stochastic, Time-Dependent Model Parameters

Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits. The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes. The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes. References: Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009), A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model, Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, <doi:10.1111/j.1467-9876.2009.00678.x> Reichert, P., and Mieleitner, J. (2009), Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45, W10402, <doi:10.1029/2009WR007814> Reichert, P., Ammann, L. and Fenicia, F. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, <doi:10.1029/2020WR028311> Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation.

Maintained by Peter Reichert. Last updated 2 years ago.

9.0 match 1.00 score 3 scripts

uncertaintyquantification

FastGaSP:Fast and Exact Computation of Gaussian Stochastic Process

Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), <doi:10.48550/arXiv.2407.10089>; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), <doi:10.48550/arXiv.2501.01324>.

Maintained by Mengyang Gu. Last updated 1 months ago.

cpp

3.4 match 2.18 score 25 scripts 1 dependents

ericgoolsby

Rphylopars:Phylogenetic Comparative Tools for Missing Data and Within-Species Variation

Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.

Maintained by Eric W. Goolsby. Last updated 3 years ago.

openblas cpp

1.0 match 31 stars 6.35 score 73 scripts

peterreichert

ecosim:Toolbox for Aquatic Ecosystem Modeling

Classes and methods for implementing aquatic ecosystem models, for running these models, and for visualizing their results.

Maintained by Peter Reichert. Last updated 2 years ago.

3.8 match 2 stars 1.48 score 15 scripts

ostirrup

covBM:Brownian Motion Processes for 'nlme'-Models

Allows Brownian motion, fractional Brownian motion, and integrated Ornstein-Uhlenbeck process components to be added to linear and non-linear mixed effects models using the structures and methods of the 'nlme' package.

Maintained by Oliver Stirrup. Last updated 9 years ago.

1.0 match 3.00 score 10 scripts

workshop-brg

abmR:Agent-Based Models in R

Supplies tools for running agent-based models (ABM) in R, as discussed in Gochanour et al. (2022) <doi:10.1111/2041-210X.14014>. The package contains two movement functions, each of which is based on the Ornstein-Uhlenbeck (OU) model (Ornstein & Uhlenbeck, 1930) <doi:10.1103/PhysRev.36.823>. It also contains several visualization and data summarization functions to facilitate the presentation of simulation results.

Maintained by Benjamin Gochanour. Last updated 2 years ago.

jags cpp

1.6 match 1.70 score

cran

mixedsde:Estimation Methods for Stochastic Differential Mixed Effects Models

Inference on stochastic differential models Ornstein-Uhlenbeck or Cox-Ingersoll-Ross, with one or two random effects in the drift function.

Maintained by Charlotte Dion. Last updated 6 years ago.

1.1 match 1.70 score 7 scripts

caetanods

do3PCA:Probabilistic Phylogenetic Principal Component Analysis

Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. Provides methods to implement alternative models of trait evolution including Brownian motion (BM), Ornstein-Uhlenbeck (OU), Early Burst (EB), and Pagel's lambda. Also provides flexible biplot functions.

Maintained by Daniel Caetano. Last updated 6 months ago.

1.0 match 1.00 score

squidlobster

peacots:Periodogram Peaks in Correlated Time Series

Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series. Citation: Louca, Stilianos and Doebeli, Michael (2015) <doi:10.1890/14-0126.1>.

Maintained by Stilianos Louca. Last updated 11 months ago.

1.0 match 1.00 score 9 scripts

hyungsuktak

Rdrw:Univariate and Multivariate Damped Random Walk Processes

We provide a toolbox to fit and simulate a univariate or multivariate damped random walk process that is also known as an Ornstein-Uhlenbeck process or a continuous-time autoregressive model of the first order, i.e., CAR(1) or CARMA(1, 0). This process is suitable for analyzing univariate or multivariate time series data with irregularly-spaced observation times and heteroscedastic measurement errors. When it comes to the multivariate case, the number of data points (measurements/observations) available at each observation time does not need to be the same, and the length of each time series can vary. The number of time series data sets that can be modeled simultaneously is limited to ten in this version of the package. We use Kalman-filtering to evaluate the resulting likelihood function, which leads to a scalable and efficient computation in finding maximum likelihood estimates of the model parameters or in drawing their posterior samples. Please pay attention to loading the data if this package is used for astronomical data analyses; see the details in the manual. Also see Hu and Tak (2020) <arXiv:2005.08049>.

Maintained by Hyungsuk Tak. Last updated 5 years ago.

1.0 match 1.00 score

hyungsuktak

timedelay:Time Delay Estimation for Stochastic Time Series of Gravitationally Lensed Quasars

We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) <doi:10.1214/17-AOAS1027>, Tak et al. (2018) <doi:10.1080/10618600.2017.1415911>, Hu and Tak (2020) <arXiv:2005.08049>.

Maintained by Hyungsuk Tak. Last updated 5 years ago.

1.0 match 1.00 score 4 scripts