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R/IRTest_Dich.R

@@ -198,15 +198,9 @@ IRTest_Dich <- function(initialitem, data, range = c(-6,6), q = 121, model,
       ld_est <- latent_dist_est(method = latent_dist, Xk = E$Xk, posterior = E$fk, range=range)
       Xk <- ld_est$Xk
       Ak <- ld_est$posterior_density
-      # post_den <- E$fk/sum(E$fk)
-      # Xk <- E$Xk
-      # lin <- lin_inex(Xk, post_den, range = range)
-      # Xk <- lin$qp
-      # post_den <- lin$qh
 
       diff <- max(abs(I-initialitem), na.rm = TRUE)
       I <- initialitem
-      # Ak <- post_den
       message("\r","\r","Method = ",latent_dist,", EM cycle = ",iter,", Max-Change = ",diff,sep="",appendLF=FALSE)
       flush.console()
     }
@@ -243,16 +237,9 @@ IRTest_Dich <- function(initialitem, data, range = c(-6,6), q = 121, model,
       ld_est <- latent_dist_est(method = latent_dist, Xk = E$Xk, posterior = E$fk, range=range, bandwidth=bandwidth, N=N, q=q)
       Xk <- ld_est$Xk
       Ak <- ld_est$posterior_density
-      # post_den <- E$fk/sum(E$fk)
-      # Xk <- E$Xk
-      # post_den <- lin_inex(Xk, post_den, range = range)$qh
-      # nzindex <- round(post_den*N)!=0
-      # SJPI <- density(rep(Xk[nzindex], times=round(post_den*N)[nzindex]), bw = bandwidth,n=q, from = range[1], to=range[2])
-      # post_den <- lin_inex(Xk, SJPI$y/sum(SJPI$y), range = range)$qh
 
       diff <- max(abs(I-initialitem), na.rm = TRUE)
       I <- initialitem
-      # Ak <- post_den
       message("\r","\r","Method = ",latent_dist,", EM cycle = ",iter,", Max-Change = ",diff,sep="",appendLF=FALSE)
       flush.console()
     }
@@ -278,22 +265,9 @@ IRTest_Dich <- function(initialitem, data, range = c(-6,6), q = 121, model,
       ld_est <- latent_dist_est(method = latent_dist, Xk = E$Xk, posterior = E$fk, range=range, phipar=phipar)
       Xk <- ld_est$Xk
       Ak <- ld_est$posterior_density
-      # Xk <- E$Xk
-      # phipar <- nlminb(start = phipar,
-      #                  objective = DC.LL,
-      #                  gradient = DC.grad,
-      #                  theta=E$Xk,
-      #                  freq = E$fk)$par
-      #
-      # post_den <- dcurver::ddc(x = Xk, phi = phipar)
-      # post_den <- post_den/sum(post_den)
-      # lin <- lin_inex(Xk, post_den, range = range, rule = 2)
-      # Xk <- lin$qp
-      # post_den <- lin$qh
 
       diff <- max(abs(I-initialitem), na.rm = TRUE)
       I <- initialitem
-      # Ak <- post_den
       message("\r","\r","Method = ",latent_dist,", EM cycle = ",iter,", Max-Change = ",diff,sep="",appendLF=FALSE)
       flush.console()
     }

README.Rmd

@@ -25,11 +25,12 @@ _Please feel free to_ [create an issue](https://github.com/SeewooLi/IRTest/issue
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/SeewooLi/IRTest/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/SeewooLi/IRTest/actions/workflows/R-CMD-check.yaml)
 [![CRAN status](https://www.r-pkg.org/badges/version/IRTest)](https://CRAN.R-project.org/package=IRTest)
+[![downloads](https://cranlogs.r-pkg.org/badges/grand-total/IRTest)]()
 <!-- badges: end -->
 
 **IRTest** is a useful tool for $\mathcal{\color{red}{IRT}}$ (item response theory) parameter $\mathcal{\color{red}{est}}\text{imation}$, especially when the violation of normality assumption on latent distribution is suspected. \
 **IRTest** deals with uni-dimensional latent variable. \
-In **IRTest**, including the conventional assumption of normality on latent distribution, several methods can be applied for estimation of latent distribution: \
+In **IRTest**, including the conventional usage of Gaussian distribution, several methods can be applied for estimation of latent distribution: \
     + empirical histogram method, \
     + two-component Gaussian mixture distribution, \
     + Davidian curve, \
@@ -65,7 +66,7 @@ Followings are functions of **IRTest**.
 
 A simple simulation study for a Rasch model can be done in following manners:
 
-```{r library}
+```{r library, message=FALSE}
 library(IRTest)
 ```
 

README.md

@@ -12,16 +12,17 @@ potential improvements.*
 [![R-CMD-check](https://github.com/SeewooLi/IRTest/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/SeewooLi/IRTest/actions/workflows/R-CMD-check.yaml)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/IRTest)](https://CRAN.R-project.org/package=IRTest)
+[![downloads](https://cranlogs.r-pkg.org/badges/grand-total/IRTest)]()
 <!-- badges: end -->
 
 **IRTest** is a useful tool for $\mathcal{\color{red}{IRT}}$ (item
 response theory) parameter $\mathcal{\color{red}{est}}\text{imation}$,
 especially when the violation of normality assumption on latent
 distribution is suspected.  
 **IRTest** deals with uni-dimensional latent variable.  
-In **IRTest**, including the conventional assumption of normality on
-latent distribution, several methods can be applied for estimation of
-latent distribution:  
+In **IRTest**, including the conventional usage of Gaussian
+distribution, several methods can be applied for estimation of latent
+distribution:  
 + empirical histogram method,  
 + two-component Gaussian mixture distribution,  
 + Davidian curve,  
@@ -68,9 +69,6 @@ manners:
 
 ``` r
 library(IRTest)
-#> Thank you for using IRTest!
-#> To acknowledge my work, please cite the package:
-#> Li, S. (2022). IRTest: Parameter estimation of item response theory with estimation of latent distribution (Version 0.1.0). R package. URL: https://CRAN.R-project.org/package=IRTest
 ```
 
 - Data generation