Introduction to weatherMRJD

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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)
#> [1] 20.76241 18.97974 20.58004 20.44872 19.88775 17.80551

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)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> -7.4609 -1.4778  0.4688  0.0000  1.7218  5.6290

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)
#> $mean
#> [1] 25.26644
#> 
#> $sd
#> [1] 2.812044
#> 
#> $n_obs
#> [1] 100

Summary

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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