Showing 51 of total 51 results (show query)
bbolker
bbmle:Tools for General Maximum Likelihood Estimation
Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.
Maintained by Ben Bolker. Last updated 1 months ago.
25 stars 13.36 score 1.4k scripts 117 dependents
biodiverse
unmarked:Models for Data from Unmarked Animals
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Maintained by Ken Kellner. Last updated 9 days ago.
4 stars 13.02 score 652 scripts 12 dependents
nimble-dev
nimble:MCMC, Particle Filtering, and Programmable Hierarchical Modeling
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.
Maintained by Christopher Paciorek. Last updated 17 days ago.
bayesian-inferencebayesian-methodshierarchical-modelsmcmcprobabilistic-programmingopenblascpp
169 stars 12.97 score 2.6k scripts 19 dependents
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
jenniniku
gllvm:Generalized Linear Latent Variable Models
Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).
Maintained by Jenni Niku. Last updated 2 days ago.
52 stars 10.57 score 176 scripts 1 dependents
drizopoulos
GLMMadaptive:Generalized Linear Mixed Models using Adaptive Gaussian Quadrature
Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) <doi:10.1080/10618600.1995.10474663>.
Maintained by Dimitris Rizopoulos. Last updated 19 days ago.
generalized-linear-mixed-modelsmixed-effects-modelsmixed-models
61 stars 10.37 score 212 scripts 5 dependents
sbfnk
rbi:Interface to 'LibBi'
Provides a complete interface to 'LibBi', a library for Bayesian inference (see <https://libbi.org> and Murray, 2015 <doi:10.18637/jss.v067.i10> for more information). This includes functions for manipulating 'LibBi' models, for reading and writing 'LibBi' input/output files, for converting 'LibBi' output to provide traces for use with the coda package, and for running 'LibBi' to conduct inference.
Maintained by Sebastian Funk. Last updated 10 months ago.
24 stars 8.35 score 390 scripts 1 dependents
openpharma
crmPack:Object-Oriented Implementation of CRM Designs
Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.
Maintained by Daniel Sabanes Bove. Last updated 2 months ago.
21 stars 7.76 score 208 scripts
cristiancastiglione
sgdGMF:Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent
Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione, Segers, Clement, Risso (2024, <https://arxiv.org/abs/2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski, Hui, Warton, Hastie (2022, <http://jmlr.org/papers/v23/20-1104.html>).
Maintained by Cristian Castiglione. Last updated 24 days ago.
10 stars 7.75 score 108 scripts
calvagone
campsis:Generic PK/PD Simulation Platform CAMPSIS
A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R packages 'rxode2' and 'mrgsolve'. CAMPSIS provides an abstraction layer over the underlying processes of writing a PK/PD model, assembling a custom dataset and running a simulation. CAMPSIS has a strong dependency to the R package 'campsismod', which allows to read/write a model from/to files and adapt it further on the fly in the R environment. Package 'campsis' allows the user to assemble a dataset in an intuitive manner. Once the userโs dataset is ready, the package is in charge of preparing the simulation, calling 'rxode2' or 'mrgsolve' (at the user's choice) and returning the results, for the given model, dataset and desired simulation settings.
Maintained by Nicolas Luyckx. Last updated 2 months ago.
8 stars 7.52 score 93 scripts
spatpomp-org
spatPomp:Inference for Spatiotemporal Partially Observed Markov Processes
Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The 'spatPomp' package extends 'pomp' to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) <doi:10.48550/arXiv.2101.01157> for further description of the package.
Maintained by Edward Ionides. Last updated 4 months ago.
2 stars 7.38 score 93 scripts
thomasp85
particles:A Graph Based Particle Simulator Based on D3-Force
Simulating particle movement in 2D space has many application. The 'particles' package implements a particle simulator based on the ideas behind the 'd3-force' 'JavaScript' library. 'particles' implements all forces defined in 'd3-force' as well as others such as vector fields, traps, and attractors.
Maintained by Thomas Lin Pedersen. Last updated 4 months ago.
d3jsgraph-layoutnetworknetwork-visualizationparticlessimulationcpp
119 stars 7.19 score 43 scripts
optad
adoptr:Adaptive Optimal Two-Stage Designs
Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.
Maintained by Maximilian Pilz. Last updated 6 months ago.
1 stars 7.09 score 39 scripts 1 dependents
yuimaproject
yuima:The YUIMA Project Package for SDEs
Simulation and Inference for SDEs and Other Stochastic Processes.
Maintained by Stefano M. Iacus. Last updated 3 days ago.
9 stars 7.02 score 92 scripts 2 dependents
roustant
DiceKriging:Kriging Methods for Computer Experiments
Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.
Maintained by Olivier Roustant. Last updated 4 years ago.
4 stars 6.99 score 526 scripts 37 dependentskingaa
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 5 months ago.
adaptive-regimebrownian-motionornstein-uhlenbeckornstein-uhlenbeck-modelsouchphylogenetic-comparative-hypothesesphylogenetic-comparative-methodsphylogenetic-datareact
15 stars 6.87 score 68 scripts 4 dependents
ingmarvisser
depmixS4:Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).
Maintained by Ingmar Visser. Last updated 4 years ago.
12 stars 6.85 score 308 scripts 4 dependents
flr
FLa4a:A Simple and Robust Statistical Catch at Age Model
A simple and robust statistical Catch at Age model that is specifically designed for stocks with intermediate levels of data quantity and quality.
Maintained by Ernesto Jardim. Last updated 8 days ago.
12 stars 6.71 score 177 scripts 2 dependents
sonsoleslp
tna:Transition Network Analysis (TNA)
Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. TNA statistical techniques (e.g., bootstrapping and permutation tests) ensure the reliability of observed insights and confirm that identified dynamics are meaningful. See (Saqr et al., 2025) <doi:10.1145/3706468.3706513> for more details on TNA.
Maintained by Sonsoles Lรณpez-Pernas. Last updated 3 days ago.
educational-data-mininglearning-analyticsmarkov-modeltemporal-analysis
4 stars 6.51 score 5 scripts
blue-matter
SAMtool:Stock Assessment Methods Toolkit
Simulation tools for closed-loop simulation are provided for the 'MSEtool' operating model to inform data-rich fisheries. 'SAMtool' provides a conditioning model, assessment models of varying complexity with standardized reporting, model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation.
Maintained by Quang Huynh. Last updated 1 months ago.
3 stars 6.39 score 36 scripts 1 dependents
thevaachandereng
bayesCT:Simulation and Analysis of Adaptive Bayesian Clinical Trials
Simulation and analysis of Bayesian adaptive clinical trials for binomial, continuous, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors.
Maintained by Thevaa Chandereng. Last updated 5 years ago.
adaptivebayesian-methodsbayesian-trialclinical-trialsstatistical-power
14 stars 6.30 score 36 scripts
cvasi-tktd
cvasi:Calibration, Validation, and Simulation of TKTD Models
Eases the use of ecotoxicological effect models. Can simulate common toxicokinetic-toxicodynamic (TK/TD) models such as General Unified Threshold models of Survival (GUTS) and Lemna. It can derive effects and effect profiles (EPx) from scenarios. It supports the use of 'tidyr' workflows employing the pipe symbol. Time-consuming tasks can be parallelized.
Maintained by Nils Kehrein. Last updated 10 days ago.
ecotoxicologymodelingsimulation
2 stars 6.27 score 12 scripts
miguel-porto
SiMRiv:Simulating Multistate Movements in River/Heterogeneous Landscapes
Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of 'Movement Ecology' hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics.
Maintained by Miguel Porto. Last updated 7 months ago.
animal-movementheterogeneous-landscapesmovement-ecologyriver-networkssimulation
15 stars 6.08 score 27 scripts 1 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 scripts
tirgit
missCompare:Intuitive Missing Data Imputation Framework
Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.
Maintained by Tibor V. Varga. Last updated 4 years ago.
comparisoncomparison-benchmarksimputationimputation-algorithmimputation-methodsimputationskolmogorov-smirnovmissingmissing-datamissing-data-imputationmissing-status-checkmissing-valuesmissingnesspost-imputation-diagnosticsrmse
39 stars 5.89 score 40 scripts
mpiktas
midasr:Mixed Data Sampling Regression
Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.
Maintained by Vaidotas Zemlys-Baleviฤius. Last updated 3 years ago.
77 stars 5.76 score 150 scripts
bioc
ceRNAnetsim:Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA)
This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables.
Maintained by Selcen Ari Yuka. Last updated 5 months ago.
networkinferencesystemsbiologynetworkgraphandnetworktranscriptomicscernamirnanetwork-biologynetwork-simulatortcgatidygraphtidyverse
4 stars 5.76 score 12 scriptsbioc
CNORode:ODE add-on to CellNOptR
Logic based ordinary differential equation (ODE) add-on to CellNOptR.
Maintained by Attila Gabor. Last updated 5 months ago.
immunooncologycellbasedassayscellbiologyproteomicsbioinformaticstimecourse
5.74 score 37 scripts 1 dependents
silvaneojunior
kDGLM:Bayesian Analysis of Dynamic Generalized Linear Models
Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<doi:10.48550/arXiv.2201.05387> and dos Santos Jr. et al. (2024)<doi:10.48550/arXiv.2403.13069>.
Maintained by Silvaneo dos Santos Jr.. Last updated 10 days ago.
2 stars 5.70 score 9 scripts
ianjonsen
bsam:Bayesian State-Space Models for Animal Movement
Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location filtering, location filtering and behavioural state estimation, and their hierarchical versions. The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged to explore tools available in packages such as 'coda' and 'boa'.
Maintained by Ian Jonsen. Last updated 9 months ago.
17 stars 5.42 score 31 scripts
tesselle
tabula:Analysis and Visualization of Archaeological Count Data
An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). It allows to easily visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.
Maintained by Nicolas Frerebeau. Last updated 25 days ago.
data-visualizationarchaeologyarchaeological-science
5.10 score 38 scripts 1 dependents
parksw3
fitode:Tools for Ordinary Differential Equations Model Fitting
Methods and functions for fitting ordinary differential equations (ODE) model in 'R'. Sensitivity equations are used to compute the gradients of ODE trajectories with respect to underlying parameters, which in turn allows for more stable fitting. Other fitting methods, such as MCMC (Markov chain Monte Carlo), are also available.
Maintained by Sang Woo Park. Last updated 1 months ago.
6 stars 5.01 score 34 scriptsbioc
rsbml:R support for SBML, using libsbml
Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models.
Maintained by Michael Lawrence. Last updated 30 days ago.
graphandnetworkpathwaysnetworklibsbmlcpp
4.71 score 19 scripts 1 dependents
zjg540066169
AuxSurvey:Survey Analysis with Auxiliary Discretized Variables
Probability surveys often use auxiliary continuous data from administrative records, but the utility of this data is diminished when it is discretized for confidentiality. We provide a set of survey estimators to make full use of information from the discretized variables. See Williams, S.Z., Zou, J., Liu, Y., Si, Y., Galea, S. and Chen, Q. (2024), Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data. Statistics in Medicine, 43: 5803-5813. <doi:10.1002/sim.10270> for details.
Maintained by Jungang Zou. Last updated 3 months ago.
auxilary-variablescategorical-variablessurvey-analysis
1 stars 4.70 score 5 scriptstesselle
kairos:Analysis of Chronological Patterns from Archaeological Count Data
A toolkit for absolute and relative dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site.
Maintained by Nicolas Frerebeau. Last updated 25 days ago.
chronologymatrix-seriationarchaeologyarchaeological-science
4.66 score 11 scripts 1 dependents
yxlin
ggdmc:Cognitive Models
The package provides tools to fit the LBA, DDM, PM and 2-D diffusion models, using the population-based Markov Chain Monte Carlo.
Maintained by Yi-Shin Lin. Last updated 8 months ago.
19 stars 4.66 score 24 scriptsflr
FLSAM:An Implementation of the State-Space Assessment Model for FLR
This package provides an FLR wrapper to the SAM state-space assessment model.
Maintained by N.T. Hintzen. Last updated 4 months ago.
4 stars 4.51 score 406 scripts
r-forge
distrSim:Simulation Classes Based on Package 'distr'
S4-classes for setting up a coherent framework for simulation within the distr family of packages.
Maintained by Peter Ruckdeschel. Last updated 2 months ago.
4.16 score 7 scripts 3 dependents
djbetancourt-gh
funGp:Gaussian Process Models for Scalar and Functional Inputs
Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). An in-depth tutorial in the use of funGp is provided in Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and Metamodeling background is provided in Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>. The algorithm for structural parameter optimization is described in <https://hal.science/hal-02532713>.
Maintained by Jose Betancourt. Last updated 11 months ago.
4 stars 3.78 score 2 scripts
yannrichet-asnr
rlibkriging:Kriging Models using the 'libKriging' Library
Interface to 'libKriging' 'C++' library <https://github.com/libKriging> that should provide most standard Kriging / Gaussian process regression features (like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages). 'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, 'lbfgsb_cpp' is a 'C++' port around by Pascal Have of 'lbfgsb' library (BSD-3 license) by Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.
Maintained by Yann Richet. Last updated 2 months ago.
3.40 score 126 scripts
qpmnguyen
SBICgraph:Structural Bayesian Information Criterion for Graphical Models
This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review). In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.
Maintained by Quang Nguyen. Last updated 4 years ago.
2.70 score 3 scripts
uncertaintyquantification
RobustGaSP:Robust Gaussian Stochastic Process Emulation
Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.
Maintained by Mengyang Gu. Last updated 1 years ago.
2.35 score 75 scripts 1 dependentsroustant
kergp:Gaussian Process Laboratory
Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation.
Maintained by Olivier Roustant. Last updated 4 months ago.
1 stars 1.83 score 67 scripts
jto888
CARMS:Continuous Time Markov Rate Modeling for Reliability Analysis
Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation. Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3).
Maintained by Jacob Ormerod. Last updated 12 months ago.
1.70 score 50 scripts
cran
spectralGP:Approximate Gaussian Processes Using the Fourier Basis
Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers.
Maintained by Chris Paciorek. Last updated 10 years ago.
1 stars 1.00 score
adrianozambom
VLMCX:Variable Length Markov Chain with Exogenous Covariates
Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) <doi:10.1111/jtsa.12615>.
Maintained by Adriano Zanin Zambom Developer. Last updated 1 years ago.
1.00 score
nicolasv-dev
drimmR:Estimation, Simulation and Reliability of Drifting Markov Models
Performs the drifting Markov models (DMM) which are non-homogeneous Markov models designed for modeling the heterogeneities of sequences in a more flexible way than homogeneous Markov chains or even hidden Markov models. In this context, we developed an R package dedicated to the estimation, simulation and the exact computation of associated reliability of drifting Markov models. The implemented methods are described in Vergne, N. (2008), <doi:10.2202/1544-6115.1326> and Barbu, V.S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8> .
Maintained by Nicolas Vergne. Last updated 4 years ago.
1.00 score