Releases · tskit-dev/tstrait

[0.1.0] - 2024-03-07

sim_genetic function is no longer supported, and users should be using genetic_value function instead. The new genetic_value function uses trait dataframe as an input, but random_seed is not a necessary argument, as there is no randomness involved. The frequency dependence architecture is implemented in sim_trait function instead of sim_genetic function, so users should put the alpha parameter in sim_trait function instead.
negative input in exponential and gamma distribution trait models are no longer supported, and users should be using random_sign instead {pr}114

Remove num_causal dependence on simulating effect sizes {pr}107
Add options to simulate effect sizes from random_sign in fixed value trait model {pr}109
Add frequency dependence architecture in sim_trait function, and allele frequency is given as an output as well {pr}111
Implement genetic_value function to compute genetic values based on the trait dataframe. There is no randomness involved, and frequency dependence architecture is not implemented {pr}112
Add option to input the causal site IDs, instead of randomly selecting them in sim_phenotype and sim_trait functions {pr}124
Add normalise_phenotypes function to normalize the simulated phenotypes {pr}130
Add delta degrees of freedom input in normalise_phenotypes function {pr}136
Add normalise_genetic_value function to normalize the genetic values {pr}145

Raise error when there are no individuals {pr}97
Raise error when incorrect values are given in the num_causal argument {pr}99
Remove # pragma: no cover in certain functions {pr}119
Modify default input arguments of sim_trait, sim_env and sim_phenotype functions {pr}120
Add verification.py for statistical tests {pr}129
Add statistical tests against external simulators {pr}132
Change the dtype of trait_id input in genetic_value function {pr}134
Add density plot in verification.py {pr}138
Add multithreading in verification.py {pr}139
Conduct exact tests against AlphaSimR, simplePHENOTYPES and the simulation framework described in ARG-Needle paper in verification.py {pr}140