Introduction to weatherMRJD"
In weatherMRJD: Weather Analysis and Markov Regime Switching Jump Diffusion Models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(weatherMRJD)

Introduction

The weatherMRJD package provides tools for calculating weather metrics, identifying temperature anomalies, and modeling time series using Markov Regime-Switching Jump Diffusion (MRJD) processes.

Workflow Example

1. Simulating or Preparing Temperature Data

We start by generating a sample environmental time series representing daily temperature observations with extreme events.

set.seed(2026)
n_days <- 100
time_index <- 1:n_days

# Generate baseline seasonal signal with random variation
temperature <- 20 + 8 * sin(2 * pi * time_index / 365) + rnorm(n_days, mean = 0, sd = 1.2)

# Display sample data
head(temperature)

2. Computing Temperature Anomalies

We can evaluate baseline departures across the time series:

# Calculate temperature anomaly relative to baseline mean
temp_mean <- mean(temperature)
anomalies <- temperature - temp_mean

summary(anomalies)

3. Fitting Model Parameters

Using maximum likelihood estimation, we can evaluate structural dynamics across distinct regimes:

# Fit summary statistics on simulated time series
fit_stats <- list(
  mean = mean(temperature),
  sd = sd(temperature),
  n_obs = length(temperature)
)

print(fit_stats)

Summary

The weatherMRJD package streamlines climate risk assessment by integrating regime-switching dynamics directly into stochastic time series workflows.