R package for statistical inference using partially observed Markov processes
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Updated
Jul 2, 2024 - R
R package for statistical inference using partially observed Markov processes
An R Package for Monte Carlo Option Pricing Algorithm for Jump Diffusion Models with Correlational Companies
Tools for Stochastic Simulation using diffusion models (R).
An R package for the stochastic simulation of processes with any marginal distribution and correlation structure
Stochastic epidemiological branching simulation
📈 📉 📈 📈 📉 Multisignal GMWM estimation and model selection for IMU
Code written as a part of MTH371 Stochastic Processes and its Applications taught my Dr. Monika Arora at IIIT Delhi in Monsoon 2018
A stokhazesthai (stochastic) process, also called a random process, is one in which outcomes are uncertain (MAT 455, ISU).
Fast R implementation of Gillespie's Stochastic Simulation Algorithm
R package to work with Markov Chain Steady-State probability vector.
R scripts for implementing different stochastic methods
This repository contains the assignments of course Stochastic Processes and Applications (MTH371) of IIIT Delhi, taught by Dr. Monika Arora in the Monsoon Semester 2021.
tibble friendly, cleverly impure functions to simulate stochastic processes
A measure of the variability of linear trends in a time-series observation, as a function of windowing length
Monte-Carlo Simulations and Analysis of Stochastic Differential Equations
R code for finding realizations (samples) of Stochastic Processes
Employed Monte Carlo simulation to model beetle population dynamics within a closed ecosystem experiencing seasonal changes driven by fluctuations in food availability and habitat.
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