My software is written in Fortran and uses gnuplot for "interactive visualization." Given are links to the reference paper (I appreciate your citations) and the code with installation hints.

2SAMPLES estimates differences in location (mean, median) and scale (standard deviation, MAD) between two samples and gives bootstrap confidence intervals. [Text (PDF)] [Code]


BREAKFIT fits a break function (trend-change model) to time series and determines standard errors by means of block bootstrap resampling. [Text (PDF)] [Code]


CLIM-X-DETECT robustly detects extremes against a time-dependent background in climate and weather time series. CLIM-X-DETECT is no longer available. Its functionality has been included in the Caliza™ package.

Link: [Caliza™]

 Exceedance Product

A collection of programs to calculate the exceedance product with bootstrap confidence levels. Program written by Karsten Kürbis (University of Leipzig, Germany). (Kürbis et al. 2009 Theoretical and Applied Climatology 98:187) [Code]


Nonparametric regression for estimation of trend, 1st derivative or 2nd derivative using the kernel method of Gasser and Müller. [Code]

Gasser T, Müller H-G (1979) Kernel estimation of regression functions. In: Gasser T, Rosenblatt M (Ed.) Smoothing Techniques for Curve Estimation. Springer, Berlin, 23–68.

Gasser T, Müller H-G (1984) Estimating regression functions and their derivatives by the kernel method. Scandinavian Journal of Statistics 11:171–185.

 PearsonT and PearsonT3

PearsonT calculates Pearson's correlation coefficient between two climate time series. This program gives bootstrap confidence intervals that are valid also in the presence of autocorrelation.
[Text (PDF)] [Code]

PearsonT3 is the same as PearsonT but with more accurate, calibrated, bootstrap confidence intervals. [Code]


RAMPFIT quantifies a climate transition using a nonlinear regression and a search for a global optimum. The bootstrap error bars for the transition parameters take autocorrelation into account. [Text (PDF)] [Code]


REDFIT estimates the spectrum using the Lomb-Scargle periodogram. It uses Monte Carlo simulations for bias correction. REDFIT performs a test of the AR(1) red-noise alternative by employing a routine from TAUEST (see below). This method can be directly used for unevenly spaced climate and weather time series. Program written by Michael Schulz (University of Bremen, Germany). [Text (PDF)] [Code]


Fortran 90 source code for parallel implementation of mzran random number generator (Marsaglia and Zaman 1994). [Code]


t1 tests for trend–variability relation in high-resolution EDC time series (Pol et al. 2014 GRL). [Text (PDF)] [Code]


TAUEST fits an AR(1) model to unevenly spaced climate time series with bootstrap confidence interval. [Text (PDF)] [Code]

Manfred Mudelsee

Climate Risk Analysis