dr_families: Families for deepregression

In deepregression: Fitting Deep Distributional Regression

Families for deepregression

Description

Families for deepregression

Families for deepregression

Usage

make_tfd_dist(family, add_const = 1e-08, output_dim = 1L, trafo_list = NULL)

make_torch_dist(family, add_const = 1e-08, output_dim = 1L, trafo_list = NULL)

Arguments

family	character vector
add_const	small positive constant to stabilize calculations
output_dim	number of output dimensions of the response (larger 1 for multivariate case) (not implemented yet)
trafo_list	list of transformations for each distribution parameter. Per default the transformation listed in details is applied.

Details

To specify a custom distribution, define the a function as follows function(x) do.call(your_tfd_dist, lapply(1:ncol(x)[[1]], function(i) your_trafo_list_on_inputs[[i]]( x[,i,drop=FALSE]))) and pass it to deepregression via the dist_fun argument. Currently the following distributions are supported with parameters (and corresponding inverse link function in brackets):

• "normal" : normal distribution with location (identity), scale (exp)

• "bernoulli" : bernoulli distribution with logits (identity)

• "bernoulli_prob" : bernoulli distribution with probabilities (sigmoid)

• "beta" : beta with concentration 1 = alpha (exp) and concentration 0 = beta (exp)

• "betar" : beta with mean (sigmoid) and scale (sigmoid)

• "cauchy" : location (identity), scale (exp)

• "chi2" : cauchy with df (exp)

• "chi" : cauchy with df (exp)

• "exponential" : exponential with lambda (exp)

• "gamma" : gamma with concentration (exp) and rate (exp)

• "gammar" : gamma with location (exp) and scale (exp), following gamlss.dist::GA, which implies that the expectation is the location, and the variance of the distribution is the location^2 scale^2

• "gumbel" : gumbel with location (identity), scale (exp)

• "half_cauchy" : half cauchy with location (identity), scale (exp)

• "half_normal" : half normal with scale (exp)

• "horseshoe" : horseshoe with scale (exp)

• "inverse_gamma" : inverse gamma with concentation (exp) and rate (exp)

• "inverse_gamma_ls" : inverse gamma with location (exp) and variance (1/exp)

• "inverse_gaussian" : inverse Gaussian with location (exp) and concentation (exp)

• "laplace" : Laplace with location (identity) and scale (exp)

• "log_normal" : Log-normal with location (identity) and scale (exp) of underlying normal distribution

• "logistic" : logistic with location (identity) and scale (exp)

• "negbinom" : neg. binomial with count (exp) and prob (sigmoid)

• "negbinom_ls" : neg. binomail with mean (exp) and clutter factor (exp)

• "pareto" : Pareto with concentration (exp) and scale (1/exp)

• "pareto_ls" : Pareto location scale version with mean (exp) and scale (exp), which corresponds to a Pareto distribution with parameters scale = mean and concentration = 1/sigma, where sigma is the scale in the pareto_ls version

• "poisson" : poisson with rate (exp)

• "poisson_lograte" : poisson with lograte (identity))

• "student_t" : Student's t with df (exp)

• "student_t_ls" : Student's t with df (exp), location (identity) and scale (exp)

• "uniform" : uniform with upper and lower (both identity)

• "zinb" : Zero-inflated negative binomial with mean (exp), variance (exp) and prob (sigmoid)

• "zip": Zero-inflated poisson distribution with mean (exp) and prob (sigmoid)

To specify a custom distribution, define the a function as follows function(x) do.call(your_tfd_dist, lapply(1:ncol(x)[[1]], function(i) your_trafo_list_on_inputs[[i]]( x[,i,drop=FALSE]))) and pass it to deepregression via the dist_fun argument. Currently the following distributions are supported with parameters (and corresponding inverse link function in brackets):

• "normal" : normal distribution with location (identity), scale (exp)

• "bernoulli" : bernoulli distribution with logits (identity)

• "exponential" : exponential with lambda (exp)

• "gamma" : gamma with concentration (exp) and rate (exp)

• "poisson" : poisson with rate (exp)

deepregression documentation built on Sept. 9, 2025, 5:27 p.m.