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™]

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.

Linear proxy calibration. [Code]

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]