Showing 9 of total 9 results (show query)
tylermorganwall
skpr:Design of Experiments Suite: Generate and Evaluate Optimal Designs
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
Maintained by Tyler Morgan-Wall. Last updated 22 days ago.
design-of-experimentslinear-modelslinear-regressionmonte-carlooptimal-designspowersplit-plot-designssurvival-analysiscpp
118 stars 6.89 score 35 scripts
ksawicka
spup:Spatial Uncertainty Propagation Analysis
Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally.
Maintained by Kasia Sawicka. Last updated 1 years ago.
monte-carlospatialuncertainty-analysisuncertainty-propagation
9 stars 6.31 score 57 scripts
k3jph
cmna:Computational Methods for Numerical Analysis
Provides the source and examples for James P. Howard, II, "Computational Methods for Numerical Analysis with R," <https://jameshoward.us/cmna/>, a book on numerical methods in R.
Maintained by James Howard. Last updated 4 years ago.
bisectiondifferential-equationsheat-equationinterpolationleast-squaresmatrix-factorizationmonte-carlonewtonnumerical-analysisoptimizationpartial-differential-equationsquadratureroot-findingsecantsplinestestthattraveling-salespersonwave-equation
16 stars 5.65 score 62 scripts 3 dependents
jeksterslab
semmcci:Monte Carlo Confidence Intervals in Structural Equation Modeling
Monte Carlo confidence intervals for free and defined parameters in models fitted in the structural equation modeling package 'lavaan' can be generated using the 'semmcci' package. 'semmcci' has three main functions, namely, MC(), MCMI(), and MCStd(). The output of 'lavaan' is passed as the first argument to the MC() function or the MCMI() function to generate Monte Carlo confidence intervals. Monte Carlo confidence intervals for the standardized estimates can also be generated by passing the output of the MC() function or the MCMI() function to the MCStd() function. A description of the package and code examples are presented in Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.
Maintained by Ivan Jacob Agaloos Pesigan. Last updated 3 months ago.
confidence-intervalsmonte-carlostructural-equation-modeling
2 stars 5.39 score 76 scripts
jirotubuyaki
Jdmbs:Monte Carlo Option Pricing Algorithms for Jump Diffusion Models with Correlational Companies
Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are represented as Geometric Brownian motion [Black (1973) <doi:10.1086/260062>] or jump diffusion processes [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other.
Maintained by Masashi Okada. Last updated 5 years ago.
black-scholesbrownian-motioncomputational-financederivativesfinancefinancial-analysisfinancial-engineeringjump-diffusionmonte-carlooptionoption-pricingsdestochastic-differential-equationsstochastic-processesstock-market
27 stars 5.13 score 6 scripts
prdm0
AcceptReject:Acceptance-Rejection Method for Generating Pseudo-Random Observations
Provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), <https://mcnp.lanl.gov/pdf_files/>, the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) <https://www.jstor.org/stable/4356322>, NEAL, Radford M. (2003) <https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).
Maintained by Pedro Rafael D. Marinho. Last updated 10 months ago.
monte-carlomonte-carlo-simulationrejection-samplingstatistics-librarycpp
1 stars 5.00 score 7 scriptsjeksterslab
betaMC:Monte Carlo for Regression Effect Sizes
Generates Monte Carlo confidence intervals for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm(). 'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect (Pesigan and Cheung, 2023 <doi:10.3758/s13428-023-02114-4>) and the sampling covariance matrix of regression coefficients (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>) to generate confidence intervals effect sizes in regression.
Maintained by Ivan Jacob Agaloos Pesigan. Last updated 3 months ago.
confidence-intervalsmonte-carloregression-effect-sizesstandardized-regression-coefficients
1 stars 4.27 score 22 scripts
roga11
MSTest:Hypothesis Testing for Markov Switching Models
Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2024_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20241015.pdf> that can be used to identify the number of regimes in Markov switching models.
Maintained by Gabriel Rodriguez Rondon. Last updated 1 months ago.
autoregressivebootstraphypothesis-testinglikelihood-ratio-testmarkov-chainmomentsmonte-carlonon-linearregime-switchingtime-seriesopenblascppopenmp
5 stars 4.18 score 3 scripts
louisaslett
mlmc:Multi-Level Monte Carlo
An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008) <doi:10.1287/opre.1070.0496>, Heinrich (1998) <doi:10.1006/jcom.1998.0471>, for R. This package builds on the original 'Matlab' and 'C++' implementations by Mike Giles to provide a full MLMC driver and example level samplers. Multi-core parallel sampling of levels is provided built-in.
Maintained by Louis Aslett. Last updated 5 months ago.
2 stars 3.70 score 5 scripts