This repository provide a short-term prediction using Classical Statistical Models, Space-State Models and Machine Learning Models in R

Simulators for Compartmental Models in Epidemiology

The project focuses on predicting Walmarts sales based on various variables such as store size, weekly sales, number of transactions in a day, temperature etc. Our approach to this problem includes…

❗ This is a read-only mirror of the CRAN R package repository. eSIR — Extended State-Space SIR Models

Early Epidemiological Model Parameters for COVID-19: system of ordinary differential equations (SIR model), least-squares model fitting, parameter estimation, epidemic simulation.

MCMC and Bayesian Statistics was used to study the parameters of the SIR model

MCMC samplers for Bayesian estimation in Python, including Metropolis-Hastings, NUTS, and Slice

Breast Cancer Detection Using Deep Learning and Machine Learning

Implementation of the algorithms: Metropolis-Hastings (MCMC) and Importance Resampling (Particle Filter) for a simple Susceptible, Infectious, or Recovered (SIR) model that simulate a flu

Implementation of the algorithms: Metropolis-Hastings (MCMC) and Importance Resampling (Particle Filter) for a simple Susceptible, Infectious, or Recovered (SIR) model that simulate a flu

Survival Analysis of Lung Cancer Patients

Survival analysis of patients with primary biliary cirrhosis (pbc) using Cox Proportional Hazards Model, Random Survival Forests, and Fast Survival SVM.