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|Rejector is a software package for parameter value estimation and comparison of alternate models of population history via rejection-based approximate Bayesian inference. This inference method involves the calculation of summary statistics from experimental data, and then the calculation of those same statistics from numerous randomized replications of the data generated from a user-specified model. Comparison of the simulated summary statistics with those generated from the experimental data will indicate the probability that the parameter values used to generate the simulations are close to the real conditions that created the experimental data.|
|Please consult the User Guide included with each version for a detailed description of the method, examples of use and suggested applications.|
|Rejector can be compiled and run under Mac OS X, Windows, and Linux. For installation instructions, see the Rejector User Guide, or if using the binaries, just make sure all binaries are in the same folder. Please read the guide before using!|
|Rejector2 uses SIMCOAL2 or msHOT for coalescent simulation. Rejector uses only SIMCOAL2. All references to either version should also include one of the following references:|
|Laval, G. and L. Excoffier (2004). "SIMCOAL 2.0: a program to simulate genomic diversity over large recombining regions in a subdivided population with a complex history." Bioinformatics (Oxford) 20(15): 2485.|
|Rejector2 uses msHOT for coalescent simulation. All references to either version should also include one of the following references:|
|Hellenthal and Stephens. msHOT: modifying Hudson's ms simulator to incorporate crossover and gene conversion hotspots. Bioinformatics (2006) vol. 23 (4) pp. 520-521|
|Post-processing and visualization of output can be done using included scripts designed to be run within the R statstical software package. The Rejector packages were written by and are maintained by Matthew Jobin at the Department of Anthropology, Santa Clara University, Santa Clara, CA.|
|Post-processing and visualization of output can be done using included scripts designed to be run within the R statstical software package.|