Get help information for trend kernels and bases
Examples
# Get information about exponential increasing kernel
trend_help(kernel = "exp_incr")
#> Description:
#> Increasing exponential kernel: k = 1 - exp(-d_ei * c)
#>
#> Default transformations (in order):
#> list(d_ei = "exp")
#>
#> Available bases, first is the default:
#> lin, exp_lin, centered
#>
# Get information about linear base
trend_help(base = "lin")
#> Description:
#> Linear base: parameter + w * k
#>
#> Default transformations:
#> list(w = "identity")
#>
# Return available kernel and base types
trend_help()
#> Available kernels:
#> custom: Custom C++ kernel: provided via register_trend().
#> lin_decr: Decreasing linear kernel: k = -c
#> lin_incr: Increasing linear kernel: k = c
#> exp_decr: Decreasing exponential kernel: k = exp(-d_ed * c)
#> exp_incr: Increasing exponential kernel: k = 1 - exp(-d_ei * c)
#> pow_decr: Decreasing power kernel: k = (1 + c)^(-d_pd)
#> pow_incr: Increasing power kernel: k = 1 - (1 + c)^(-d_pi)
#> poly2: Quadratic polynomial: k = d1 * c + d2 * c^2
#> poly3: Cubic polynomial: k = d1 * c + d2 * c^2 + d3 * c^3
#> poly4: Quartic polynomial: k = d1 * c + d2 * c^2 + d3 * c^3 + d4 * c^4
#> delta: Standard delta rule kernel: k = q[i].
#> Updates q[i] = q[i-1] + alpha * (c[i-1] - q[i-1]).
#> Parameters: q0 (initial value), alpha (learning rate).
#> delta2kernel: Dual kernel delta rule: k = q[i].
#> Combines fast and slow learning rates
#> and switches between them based on dSwitch.
#> Parameters: q0 (initial value), alphaFast (fast learning rate),
#> propSlow (alphaSlow = propSlow * alphaFast), dSwitch (switch threshold).
#> delta2lr: Dual learning rate delta rule: k = q[i].
#> Like the standard delta rule, but with separate
#> learning rates for positive and negative prediction errors.
#> Parameters: q0 (initial value), alphaPos (learning rate for positive PEs),
#> alphaNeg (learning rate for negative PEs).
#>
#> Available base types:
#> lin: Linear base: parameter + w * k
#> exp_lin: Exponential linear base: exp(parameter) + exp(w) * k
#> centered: Centered mapping: parameter + w*(k - 0.5)
#> add: Additive base: parameter + k
#> identity: Identity base: k
#>
#> Phase options:
#> premap: Trend is applied before parameter mapping. This means the trend parameters
#> are mapped first, then used to transform cognitive model parameters before
#> their mapping.
#> pretransform: Trend is applied after parameter mapping but before transformations.
#> Cognitive model parameters are mapped first, then trend is applied,
#> followed by transformations.
#> posttransform: Trend is applied after both mapping and transformations.
#> Cognitive model parameters are mapped and transformed first,
#> then trend is applied.