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Get help information for trend kernels and bases

Usage

trend_help(kernel = NULL, base = NULL, ...)

Arguments

kernel

Character string specifying the kernel type to get information about

base

Character string specifying the base type to get information about

...

Additional arguments

Value

Formatted trend information

Examples

# Get information about exponential increasing kernel
trend_help(kernel = "exp_incr")
#> Description: 
#> Increasing exponential kernel: k = 1 - exp(-d * 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 + b * k 
#>  
#> Default transformations: 
#> list(B0 = "identity")
#>  

# Return available kernel and base types
trend_help()
#> Available kernels:
#>   lin_decr: Decreasing linear kernel: k = -c
#>   lin_incr: Increasing linear kernel: k = c
#>   exp_decr: Decreasing exponential kernel: k = exp(-d * c)
#>   exp_incr: Increasing exponential kernel: k = 1 - exp(-d * c)
#>   pow_decr: Decreasing power kernel: k = (1 + c)^(-d)
#>   pow_incr: Increasing power kernel: k = 1 - (1 + c)^(-d)
#>   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: Updates q[i] = q[i-1] + alpha * (c[i-1] - q[i-1]). Parameters: q0 (initial value), alpha (learning rate).
#>   delta2: Dual kernel delta rule: 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).
#> 
#> Available base types:
#>   lin: Linear base: parameter + b * k
#>   exp_lin: Exponential linear base: exp(parameter) + exp(b) * k
#>   centered: Centered mapping: parameter + b*(k - 0.5)
#>   add: Additive base: parameter + k
#>   identity: Identity base: k
#> 
#> Trend 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.