fix docu

Author: davidruegamer

R/families.R

@@ -25,47 +25,47 @@ tfmult <- function(x,y) tf$math$multiply(x,y)
 #' with parameters (and corresponding inverse link function in brackets):
 #'
 #' \itemize{
-#'  \item{\code{"normal": }}{normal distribution with location (identity), scale (exp)}
-#'  \item{\code{"bernoulli": }}{bernoulli distribution with logits (identity)}
-#'  \item{\code{"bernoulli_prob": }}{bernoulli distribution with probabilities (sigmoid)}
-#'  \item{\code{"beta": }}{beta with concentration 1 = alpha (exp) and concentration
-#'  0 = beta (exp)}
-#'  \item{\code{"betar": }}{beta with mean (sigmoid) and scale (sigmoid)}
-#'  \item{\code{"cauchy": }}{location (identity), scale (exp)}
-#'  \item{\code{"chi2": }}{cauchy with df (exp)}
-#'  \item{\code{"chi": }}{cauchy with df (exp)}
-#'  \item{\code{"exponential": }}{exponential with lambda (exp)}
-#'  \item{\code{"gamma": }}{gamma with concentration (exp) and rate (exp)}
-#'  \item{\code{"gammar": }}{gamma with location (exp) and scale (exp), following
+#'  \item \code{"normal"} : normal distribution with location (identity), scale (exp)
+#'  \item \code{"bernoulli"} : bernoulli distribution with logits (identity)
+#'  \item \code{"bernoulli_prob"} : bernoulli distribution with probabilities (sigmoid)
+#'  \item \code{"beta"} : beta with concentration 1 = alpha (exp) and concentration
+#'  0 = beta (exp)
+#'  \item \code{"betar"} : beta with mean (sigmoid) and scale (sigmoid)
+#'  \item \code{"cauchy"} : location (identity), scale (exp)
+#'  \item \code{"chi2"} : cauchy with df (exp)
+#'  \item \code{"chi"} : cauchy with df (exp)
+#'  \item \code{"exponential"} : exponential with lambda (exp)
+#'  \item \code{"gamma"} : gamma with concentration (exp) and rate (exp)
+#'  \item \code{"gammar"} : gamma with location (exp) and scale (exp), following
 #'  \code{gamlss.dist::GA}, which implies that the expectation is the location, 
-#'  and the variance of the distribution is the \code{location^2 scale^2}}
-#'  \item{\code{"gumbel": }}{gumbel with location (identity), scale (exp)}
-#'  \item{\code{"half_cauchy": }}{half cauchy with location (identity), scale (exp)}
-#'  \item{\code{"half_normal": }}{half normal with scale (exp)}
-#'  \item{\code{"horseshoe": }}{horseshoe with scale (exp)}
-#'  \item{\code{"inverse_gamma": }}{inverse gamma with concentation (exp) and rate (exp)}
-#'  \item{\code{"inverse_gamma_ls": }}{inverse gamma with location (exp) and variance (1/exp)}
-#'  \item{\code{"inverse_gaussian": }}{inverse Gaussian with location (exp) and concentation
-#'  (exp)}
-#'  \item{\code{"laplace": }}{Laplace with location (identity) and scale (exp)}
-#'  \item{\code{"log_normal": }}{Log-normal with location (identity) and scale (exp) of
-#'  underlying normal distribution}
-#'  \item{\code{"logistic": }}{logistic with location (identity) and scale (exp)}
-#'  \item{\code{"negbinom": }}{neg. binomial with count (exp) and prob (sigmoid)}
-#'  \item{\code{"negbinom_ls": }}{neg. binomail with mean (exp) and clutter factor (exp)}
-#'  \item{\code{"pareto": }}{Pareto with concentration (exp) and scale (1/exp)} 
-#'  \item{\code{"pareto_ls": }}{Pareto location scale version with mean (exp) 
+#'  and the variance of the distribution is the \code{location^2 scale^2}
+#'  \item \code{"gumbel"} : gumbel with location (identity), scale (exp)
+#'  \item \code{"half_cauchy"} : half cauchy with location (identity), scale (exp)
+#'  \item \code{"half_normal"} : half normal with scale (exp)
+#'  \item \code{"horseshoe"} : horseshoe with scale (exp)
+#'  \item \code{"inverse_gamma"} : inverse gamma with concentation (exp) and rate (exp)
+#'  \item \code{"inverse_gamma_ls"} : inverse gamma with location (exp) and variance (1/exp)
+#'  \item \code{"inverse_gaussian"} : inverse Gaussian with location (exp) and concentation
+#'  (exp)
+#'  \item \code{"laplace"} : Laplace with location (identity) and scale (exp)
+#'  \item \code{"log_normal"} : Log-normal with location (identity) and scale (exp) of
+#'  underlying normal distribution
+#'  \item \code{"logistic"} : logistic with location (identity) and scale (exp)
+#'  \item \code{"negbinom"} : neg. binomial with count (exp) and prob (sigmoid)
+#'  \item \code{"negbinom_ls"} : neg. binomail with mean (exp) and clutter factor (exp)
+#'  \item \code{"pareto"} : Pareto with concentration (exp) and scale (1/exp)
+#'  \item \code{"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}
-#'  \item{\code{"poisson": }}{poisson with rate (exp)}
-#'  \item{\code{"poisson_lograte": }}{poisson with lograte (identity))}
-#'  \item{\code{"student_t": }}{Student's t with df (exp)}
-#'  \item{\code{"student_t_ls": }}{Student's t with df (exp), location (identity) and
-#'  scale (exp)}
-#'  \item{\code{"uniform": }}{uniform with upper and lower (both identity)}
-#'  \item{\code{"zinb": }}{Zero-inflated negative binomial with mean (exp), 
-#'  variance (exp) and prob (sigmoid)}
-#'  \item{\code{"zip":  }}{Zero-inflated poisson distribution with mean (exp) and prob (sigmoid)}
+#'  and concentration = 1/sigma, where sigma is the scale in the pareto_ls version
+#'  \item \code{"poisson"} : poisson with rate (exp)
+#'  \item \code{"poisson_lograte"} : poisson with lograte (identity))
+#'  \item \code{"student_t"} : Student's t with df (exp)
+#'  \item \code{"student_t_ls"} : Student's t with df (exp), location (identity) and
+#'  scale (exp)
+#'  \item \code{"uniform"} : uniform with upper and lower (both identity)
+#'  \item \code{"zinb"} : Zero-inflated negative binomial with mean (exp), 
+#'  variance (exp) and prob (sigmoid)
+#'  \item \code{"zip":  } Zero-inflated poisson distribution with mean (exp) and prob (sigmoid)
 #' }
 #' @param add_const small positive constant to stabilize calculations
 #' @param trafo_list list of transformations for each distribution parameter.
@@ -552,59 +552,59 @@ tfd_mvr <- function(loc, scale,
 
 }
 
-#' 
-#' # Implementation of a distribution-like layer for (Quasi-)Tweedie
-#' tfd_tweedie <- function(loc, phi, p = 1.5, quasi = FALSE,
-#'                          validate_args = FALSE,
-#'                          allow_nan_stats = TRUE,
-#'                          name = "Tweedie")
-#' {
-#'   
-#'   args <- list(
-#'     loc = loc,
-#'     scale = phi,
-#'     var_power = p,
-#'     quasi = quasi,
-#'     validate_args = validate_args,
-#'     allow_nan_stats = allow_nan_stats,
-#'     name = name
-#'   )
-#'   
-#'   python_path <- system.file("python", package = "deepregression")
-#'   distributions <- reticulate::import_from_path("distributions", path = python_path)
-#'   
-#'   return(do.call(distributions$Tweedie, args))
-#'   
-#' }
-#' 
-#' #' tfd_distfun for (Quasi-)Tweedie to allow for flexible p
-#' #' @param p integer; defines distribution
-#' #' @param quasi logical; whether to use quasi-likelihood or deviance resids
-#' #' @param output_dim integer; currently only univariate responses supported
-#' #' @export
-#' #' 
-#' tweedie <- function(p, quasi = FALSE, output_dim = 1L,
-#'                     linkfun_mean = function(x) tf$add(1e-8, tf$math$exp(x)),
-#'                     linkfun_phi = function(x) tf$add(1e-8, tf$math$exp(x)))
-#' {
-#'   
-#'   tfd_dist <- function(l, s) tfd_tweedie(loc = l, phi = s, p = p, quasi = quasi)
-#'   trafo_list <- list(linkfun_mean, linkfun_phi) 
-#'   dist_dim <- 2L
-#'   ret_fun <- function(x) 
-#'     do.call(tfd_dist,
-#'             lapply(1:(x$shape[[2]]/output_dim),
-#'                    function(i)
-#'                      trafo_list[[i]](
-#'                        tf_stride_cols(x,(i-1L)*output_dim+1L,
-#'                                       (i-1L)*output_dim+output_dim)))
-#'     )
-#'   attr(ret_fun, "nrparams_dist") <- 2L
-#'   
-#'   return(ret_fun)
-#'   
-#' }
-#' 
+# 
+# # Implementation of a distribution-like layer for (Quasi-)Tweedie
+# tfd_tweedie <- function(loc, phi, p = 1.5, quasi = FALSE,
+#                          validate_args = FALSE,
+#                          allow_nan_stats = TRUE,
+#                          name = "Tweedie")
+# {
+#   
+#   args <- list(
+#     loc = loc,
+#     scale = phi,
+#     var_power = p,
+#     quasi = quasi,
+#     validate_args = validate_args,
+#     allow_nan_stats = allow_nan_stats,
+#     name = name
+#   )
+#   
+#   python_path <- system.file("python", package = "deepregression")
+#   distributions <- reticulate::import_from_path("distributions", path = python_path)
+#   
+#   return(do.call(distributions$Tweedie, args))
+#   
+# }
+# 
+# #' tfd_distfun for (Quasi-)Tweedie to allow for flexible p
+# #' @param p integer; defines distribution
+# #' @param quasi logical; whether to use quasi-likelihood or deviance resids
+# #' @param output_dim integer; currently only univariate responses supported
+# #' @export
+# #' 
+# tweedie <- function(p, quasi = FALSE, output_dim = 1L,
+#                     linkfun_mean = function(x) tf$add(1e-8, tf$math$exp(x)),
+#                     linkfun_phi = function(x) tf$add(1e-8, tf$math$exp(x)))
+# {
+#   
+#   tfd_dist <- function(l, s) tfd_tweedie(loc = l, phi = s, p = p, quasi = quasi)
+#   trafo_list <- list(linkfun_mean, linkfun_phi) 
+#   dist_dim <- 2L
+#   ret_fun <- function(x) 
+#     do.call(tfd_dist,
+#             lapply(1:(x$shape[[2]]/output_dim),
+#                    function(i)
+#                      trafo_list[[i]](
+#                        tf_stride_cols(x,(i-1L)*output_dim+1L,
+#                                       (i-1L)*output_dim+output_dim)))
+#     )
+#   attr(ret_fun, "nrparams_dist") <- 2L
+#   
+#   return(ret_fun)
+#   
+# }
+# 
 
 
 #' For using mean squared error via TFP