Package 'discent'

dataframe; The genetic-geographic data by deme (K)

double; lower limit value for the global "m" parameter; any "m" value encounter less than the lower bound will be replaced by the lower bound

double; upper limit value for the global "m" parameter; any "m" value encounter greater than the upper bound will be replaced by the upper bound

double; The initial deme-inbreeding parameter lower-bound to parameterize each swarm-particle ($a$), such that starting deme-inbreeding estimates are drawn from a uniform distribution $f = U_{a,b}$

double; As above, the initial deme-inbreeding parameter upper-bound to parameterize each swarm-particle ($b$).

double; The initial deme-inbreeding learning-rate lower-bound to parameterize each swarm-particle's convergence to the $f$ parameter ($a$ draw from the unifrom $f = U_{a,b}$).

double; As above, the initial deme-inbreeding learning-rate upper-bound to parameterize each swarm-particle ($b$).

double; similar to before, the initial lambda value lower-bound to parameterize each swarm-particle's convergence to the $m$ parameter ($a$ draw from the unifrom $m = U_{a,b}$).

double; As above, the initial lambda value upper-bound to parameterize each swarm-particle ($b$).

double; the "cognitive" coefficient from the PSO algorithm. Essentially, it dictates how strongly the prior particle's positions should be weighted against the entire swarm's historical positions in determining the next step of exploration.

double; the "social" coefficient from the PSO. Essentially determines how much weight or influence other particles in the swarm exert on the current particle in determing the next step of exploration.

double; the "inertia" coefficient from the PSO algorithm. Essentially, how strongly the currently velocity (i.e. the direction the particle is headed) should be weighted relative to the prior particle's and swarm's current positions (i.e. prior directions).

double; exponential decay rates for the first moment estimate in the Adam optimization algorithm

double; exponential decay rates for the second moment estimate in the Adam optimization algorithm

double; epsilon (error) for stability in the Adam optimization algorithm

integer; the number of "final" steps considered for the "final run" of the gradient descent

integer; the number of steps that a particle takes in the vanilla gradient descent algorithm given its newly initialized start parameters in order to calculate a cost for the new position. Essentially, we consider the vanilla gradient descent model at the current position for a short number of iterations to estimate the "favorability of the positions current footing" or "traction" of the current position being considered.

integer; the number of iterations or moves that the particles within the swarm are able to explore before selecting the final particle for the "final" run (note, moves is reserved for the swarm's actions, while steps is used to describe iterations in the gradient descent algorithm).

integer; the number of particles in the swarm

integer; the number of steps to keep as part of the output (i.e. if the user specifies 10, every 10th iteration will be kept)

boolean; whether geographic distances between demes should be normalized (i.e. Min-Max Feature Scaling: $X' = \frac{X - X_{min}}{X_{max} - X_{min}}$, which places the geodistances on the scale to $[0-1]$). Helps increase model stability at the expense of complicating the interpretation of the migration rate parameter.

boolean; search chain

boolean; final chain

boolean; whether the inbreeding coefficients and migration rate should be returned for every iteration or only for the final iteration. User will typically not want to store every iteration, which can be memory intensive

dataframe; The genetic-geographic data by deme (K)

named double vector; vector of start parameters.

double; A quadratic L2 explicit regularization, or penalty, parameter on "m" parameter. Note, lambda is a scalar such that: $\lambda m^2$.

double; alpha parameter for how much each "step" is weighted in the gradient descent

double; lower limit value for the global "m" parameter; any "m" value encounter less than the lower bound will be replaced by the lower bound

double; upper limit value for the global "m" parameter; any "m" value encounter greater than the upper bound will be replaced by the upper bound

double; exponential decay rates for the first moment estimate in the Adam optimization algorithm

double; exponential decay rates for the second moment estimate in the Adam optimization algorithm

double; epsilon (error) for stability in the Adam optimization algorithm

integer; the number of steps as we move down the gradient

integer; the number of steps to keep as part of the output (i.e. if the user specifies 10, every 10th iteration will be kept)

boolean; whether geographic distances between demes should be normalized (i.e. Min-Max Feature Scaling: $X' = \frac{X - X_{min}}{X_{max} - X_{min}}$, which places the geodistances on the scale to $[0-1]$). Helps increase model stability at the expense of complicating the interpretation of the migration rate parameter.

boolean; whether or not a progress bar should be shown as you iterate through steps

boolean; whether the inbreeding coefficients and migration rate should be returned for every iteration or only for the final iteration. User will typically not want to store every iteration, which can be memory intensive

DISC result from deme_inbreeding_spcoef function

DISC result from deme_inbreeding_spcoef function

further arguments passed to or from other methods.

DISCresult Simulation

further arguments passed to or from other methods.

DISC result from deme_inbreeding_spcoef function

DISC result from deme_inbreeding_spcoef function