R package for statistical inference using partially observed Markov processes
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Updated
Jun 9, 2024 - R
R package for statistical inference using partially observed Markov processes
R code for finding realizations (samples) of Stochastic Processes
Tools for Stochastic Simulation using diffusion models (R).
Employed Monte Carlo simulation to model beetle population dynamics within a closed ecosystem experiencing seasonal changes driven by fluctuations in food availability and habitat.
A measure of the variability of linear trends in a time-series observation, as a function of windowing length
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.
Monte-Carlo Simulations and Analysis of Stochastic Differential Equations
A stokhazesthai (stochastic) process, also called a random process, is one in which outcomes are uncertain (MAT 455, ISU).
📈 📉 📈 📈 📉 Multisignal GMWM estimation and model selection for IMU
Stochastic epidemiological branching simulation
tibble friendly, cleverly impure functions to simulate stochastic processes
R package to work with Markov Chain Steady-State probability vector.
An R Package for Monte Carlo Option Pricing Algorithm for Jump Diffusion Models with Correlational Companies
An R package for the stochastic simulation of processes with any marginal distribution and correlation structure
Code written as a part of MTH371 Stochastic Processes and its Applications taught my Dr. Monika Arora at IIIT Delhi in Monsoon 2018
R scripts for implementing different stochastic methods
Fast R implementation of Gillespie's Stochastic Simulation Algorithm
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