for CRAN

DESCRIPTION

@@ -4,7 +4,7 @@ Version: 0.2.0
 Title: Density Ratio Estimation
 Description: Density ratio estimation.
     The estimated density ratio function can be used in many applications such as
-    anomaly detection, changepoint detection, covariate shift adaptation, and etc.
+    anomaly detection, change-point detection, covariate shift adaptation.
     The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>),
     RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>),
     and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).

README.Rmd

@@ -34,7 +34,7 @@ library(mvtnorm)
 
 **Density ratio estimation** is described as follows: for given two data samples `x1` and `x2` from unknown distributions `p(x)` and `q(x)` respectively, estimate `w(x) = p(x) / q(x)`, where `x1` and `x2` are d-dimensional real numbers.
 
-The estimated density ratio function `w(x)` can be used in many applications such as **anomaly detection** [Hido et al. 2011], **changepoint detection** [Liu et al. 2013], and **covariate shift adaptation** [Sugiyama et al. 2007].
+The estimated density ratio function `w(x)` can be used in many applications such as **anomaly detection** [Hido et al. 2011], **change-point detection** [Liu et al. 2013], and **covariate shift adaptation** [Sugiyama et al. 2007].
 Other useful applications about density ratio estimation were summarized by [Sugiyama et al. 2012].
 
 The package **densratio** provides a function `densratio()` that returns an object with a method to estimate density ratio as `compute_density_ratio()`.

README.md

@@ -27,7 +27,7 @@ d-dimensional real numbers.
 
 The estimated density ratio function `w(x)` can be used in many
 applications such as **anomaly detection** \[Hido et al. 2011\],
-**changepoint detection** \[Liu et al. 2013\], and **covariate shift
+**change-point detection** \[Liu et al. 2013\], and **covariate shift
 adaptation** \[Sugiyama et al. 2007\]. Other useful applications about
 density ratio estimation were summarized by \[Sugiyama et al. 2012\].
 
@@ -173,7 +173,7 @@ is the Gaussian (RBF) kernel.
     #> Kernel Information:
     #>   Kernel type:  Gaussian 
     #>   Number of kernels:  100 
-    #>   Bandwidth(sigma):  0.1 
+    #>   Bandwidth (sigma):  0.1 
     #>   Centers:  num [1:100, 1] 0.907 1.093 1.18 1.136 1.046 ...
     #> 
     #> Kernel Weights: