version 1.1.1

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: MLmetrics
 Type: Package
 Title: Machine Learning Evaluation Metrics
-Version: 1.1.0
+Version: 1.1.1
 Authors@R: person("Yachen", "Yan", email = "yanyachen21@gmail.com", role = c("aut", "cre"))
 Description: A collection of evaluation metrics, including loss, score and
     utility functions, that measure regression, classification and ranking performance.
@@ -14,8 +14,8 @@ License: GPL-2
 LazyData: true
 RoxygenNote: 5.0.1
 NeedsCompilation: no
-Packaged: 2016-05-01 11:07:14 UTC; Administrator
+Packaged: 2016-05-09 06:13:55 UTC; Administrator
 Author: Yachen Yan [aut, cre]
 Maintainer: Yachen Yan <yanyachen21@gmail.com>
 Repository: CRAN
-Date/Publication: 2016-05-01 18:32:12
+Date/Publication: 2016-05-13 23:57:26

MD5

@@ -1,7 +1,7 @@
-d7b7368243655ee5304ada439ca9451a *DESCRIPTION
-7effa637d79decd846a0a621308cd92f *NAMESPACE
+c25b4cbfe6164da84e7c7bd33b366d52 *DESCRIPTION
+a2f973d4508642440a922397320545f9 *NAMESPACE
 44c40b30f892bb85db6c56d93e75e4ac *R/Classification.R
-77dce5a5d732eef51c1cfc010363df7b *R/Count.R
+7967e8f036200816909e52bb6e1d38f3 *R/Count.R
 556b2f37d8641c9ed02933c54ef7c368 *R/MLmetrics.R
 d41d8cd98f00b204e9800998ecf8427e *R/Rank.R
 ade5da8623726eba10bad8bcb98351dc *R/Regression.R
@@ -27,7 +27,7 @@ e594715ad175e7d58740b41d2a3fb0ca *man/MedianAE.Rd
 337cacbd020fdea0ca40f9769e02c3b4 *man/MultiLogLoss.Rd
 ee842fd12312073d812bf0c88fe6c21d *man/NormalizedGini.Rd
 96fc35f78ff2fdccc22de51d895308e6 *man/PRAUC.Rd
-a70115966d9d9ed107edf297c5a7751b *man/Possion_LogLoss.Rd
+abf94a4bd0955312dd7cb8b781099038 *man/Poisson_LogLoss.Rd
 3f0b82a06a53da4e19499280ae8d7ff9 *man/Precision.Rd
 787ec83fe010b999aa235ff1ba14128b *man/R2_Score.Rd
 ae9c96a8338363be60d822347d8cb41e *man/RAE.Rd

NAMESPACE

@@ -20,7 +20,7 @@ export(MedianAPE)
 export(MultiLogLoss)
 export(NormalizedGini)
 export(PRAUC)
-export(Possion_LogLoss)
+export(Poisson_LogLoss)
 export(Precision)
 export(R2_Score)
 export(RAE)

R/Count.R

@@ -1,4 +1,4 @@
-#' @title Possion Log loss
+#' @title Poisson Log loss
 #'
 #' @description
 #' Compute the log loss/cross-entropy loss.
@@ -11,15 +11,15 @@
 #'                    counts = c(18,17,15,20,10,20,25,13,12))
 #' glm_poisson <- glm(counts ~ outcome + treatment,
 #'                    family = poisson(link = "log"), data = d_AD)
-#' Possion_LogLoss(y_pred = glm_poisson$fitted.values, y_true = d_AD$counts)
+#' Poisson_LogLoss(y_pred = glm_poisson$fitted.values, y_true = d_AD$counts)
 #' @export
 
-Possion_LogLoss <- function(y_pred, y_true) {
+Poisson_LogLoss <- function(y_pred, y_true) {
   eps <- 1e-15
   y_pred <- pmax(y_pred, eps)
-  Possion_LogLoss <- mean(log(gamma(y_true + 1)) + y_pred - log(y_pred) * y_true)
-  # Possion_LogLoss <- mean(-dpois(y_true, y_pred, log = TRUE))
-  return(Possion_LogLoss)
+  Poisson_LogLoss <- mean(log(gamma(y_true + 1)) + y_pred - log(y_pred) * y_true)
+  # Poisson_LogLoss <- mean(-dpois(y_true, y_pred, log = TRUE))
+  return(Poisson_LogLoss)
 }