ct_fwdpp is a free software. It is under GNU General Public License GPLv3. Any redistribution or modification should be under the terms of the GNU General Public License as published by the Free Software Foundation. This code is modified based on fwdpp by Kevin Thornton.

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Any questions or comments:

Pengyao Jiang <pyjiang2@gmail.com>

fwdpp, Eigen.

• I use fwdpp version 0.5.1 (with fixing a small bug in the #ifndef statement in fwdpp/tags/gamete_tags.hpp; alternatively, a version with this bug fixed is 0.5.3), eigen 3.3.1.

• Compiler should support C++11
• Download the required packages (only C++ header files are needed)
• Edit Makefile in this directory (change CPPFLAGS -I to the package directory)
• make all to make all the targets
• or optionally, only make specific target
  • make haploid_save_new to make the target for basic simulations with a fixed level of robustness with $\alpha$
  • make haploid_diff_init_rob_evol to make the target for simulations which can evolve robustness with $\gamma$

• For basic simulations with a fixed level of robustness with $\alpha$

  usage:

  haploid_save_new  N  theta  ngens seed nz Lv  c pv1 ga_flag ga stype out_common_name

• N: population size (as if the individuals are diploids)
• theta: 2Nu (u is the mutation rate per individual per generation)
• ngens: how many generations of forward simulations to run
• seed: the random number seed used to initialize parameters
• nz: length of z vector (phenotype vector)
• Lv: length of v vector (genotype vector)
• c: the percentage of non-zero in the T matrix
• pv1: percent of 1 in the v vector
• ga_flag: flag for $\gamma$. if gamma_flag =0 : pass $\gamma$ (next argument) as a parameter; if gamma_flag =1 : pass index of $\gamma$ in the list (the list contains 10 $\gamma$ which are equally separated by their robustness value)
• ga: $\gamma$ value
• stype: which type of simulation to run. if type=0: with optimal initial environment; if type=1, with a random initial environment
• out_common_name: the prefix for the output files.

• For simulations which can evolve robustness with $\gamma$

  usage:

  haploid_diff_init_rob_evol N theta rmb ngens  seed mu1_base nz Lv  robust_bits  c pv1 ga stype out_common_name   robust_flag  low_bound high_bound  scale_flag

• N: population size (as if the individuals are diploids)
• theta: 2Nu (u is the mutation rate per individual per generation)
• rmb: a value between 0 and 0.5, where 0.5 means random shuffling of robustness locus and the regular genotype locus, and 0 means complete linkage
• ngens: how many generations of forward simulations to run
• seed: the random number seed used to initialize parameters
• mu1_base: mutation rate per bit for the robustness locus
• nz: length of z vector (phenotype vector)
• Lv: length of v vector (genotype vector)
• robust_bits: the number of bits that encodes robustness locus
• c: the percentage of non-zero in the T matrix
• pv1: percent of 1 in the v vector
• ga: $\gamma$ value
• stype: which type of simulation to run. if type=0: with optimal initial environment; if type=1, with a random initial environment
• out_common_name: the prefix for the output files
• robust_flag: if robust_flag =1: evolve robustness with the robustness locus, if robust_flag=2, do not evolve robustness locus
• low_bound: a number that is the log2 lower bound of the $\alpha$ value
• high_bound: a number that is the log2 higher bound of the $\alpha$ value
• scale_flag: specify how robustness genotype maps to robustness set by $\alpha$ value if scale_flag = 8, it is exactly as described in the paper.

Alternatively, you can run with scripts that are in ./log directory. scripts: generates the same simulation results as in the paper. or you can run with different seed values with