diff --git a/DESCRIPTION b/DESCRIPTION
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--- /dev/null
+++ b/DESCRIPTION
@@ -0,0 +1,36 @@
+Package: practicalSigni
+Type: Package
+Title: Practical Significance Ranking of Regressors
+Version: 0.1.0
+Date: 2023-02-16
+Authors@R: c(person("Hrishikesh", "Vinod", role = c("aut", "cre"),
+      email= "vinod@fordham.edu"))
+Encoding: UTF-8
+Depends: R (>= 4.2.0), np (>= 0.60), xtable (>= 1.8), generalCorr (>=
+        1.2), NNS (>= 0.9), randomForest (>= 4.7),
+Suggests: R.rsp
+VignetteBuilder: R.rsp
+Description: Consider a possibly nonlinear nonparametric regression
+   with p regressors. We provide evaluations by 13 methods to rank
+   regressors by their practical significance or importance using 
+   various methods, including machine learning tools. Comprehensive
+   methods are as follows. 
+   m6=Generalized partial correlation coefficient or
+   GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and
+   Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>.
+   m7= a generalization of psychologists' effect size incorporating 
+   nonlinearity and many variables.
+   m8= local linear partial (dy/dxi) using the 'np' package for kernel 
+   regressions.
+   m9= partial (dy/dxi) using the 'NNS' package.
+   m10= importance measure using the 'NNS' boost function.
+   m11= Shapley Value measure of importance (cooperative game theory).
+   m12 and m13= two versions of the random forest algorithm.
+License: GPL (>= 2)
+RoxygenNote: 7.2.3
+NeedsCompilation: no
+Packaged: 2023-02-16 20:40:15 UTC; vinod
+Author: Hrishikesh Vinod [aut, cre]
+Maintainer: Hrishikesh Vinod <vinod@fordham.edu>
+Repository: CRAN
+Date/Publication: 2023-02-17 10:00:09 UTC
diff --git a/MD5 b/MD5
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--- /dev/null
+++ b/MD5
@@ -0,0 +1,17 @@
+92c1f695ab61b5e80990cdd319bfc34d *DESCRIPTION
+a32fe68dcc5cbc8ce3fca00bd5233ceb *NAMESPACE
+af0760a4e6931ee6d4eba7dfada61307 *R/effSizCut.R
+88191dcd47435a1b93d49d308d7380b4 *R/fncut.R
+bb793c2f927ef0f65325c1f5bb34cff6 *R/pracSig13.R
+4d3098a8fc36f18af32bbfa8a01e0651 *R/reportRank.R
+9d749b214937d8705a43ebf337b5b90d *R/shapleyvalue.R
+cb4924f3573383fca290a0a8e2ca5294 *build/partial.rdb
+413069f2e65b506d9ce5f353d3dde1b2 *build/vignette.rds
+9444c4cba73807f33dfd5c504eae8bd5 *inst/doc/practicalSigni-vignette.pdf
+d245490b43d80a01ad113320d14427c7 *inst/doc/practicalSigni-vignette.pdf.asis
+3c34399a2a413e3326a47930a7194bf7 *man/effSizCut.Rd
+800d600966d85d4a64388adf63dae6a4 *man/fncut.Rd
+7a69afc9d5d2cd34d022dff7ba99ab1a *man/pracSig13.Rd
+52f100cc0eb342987faa543ec8e403f0 *man/reportRank.Rd
+cd2e8d19432223a68fb2cb9eddcf6949 *man/shapleyvalue.Rd
+d245490b43d80a01ad113320d14427c7 *vignettes/practicalSigni-vignette.pdf.asis
diff --git a/NAMESPACE b/NAMESPACE
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--- /dev/null
+++ b/NAMESPACE
@@ -0,0 +1,25 @@
+# Generated by roxygen2: do not edit by hand
+
+export(effSizCut)
+export(fncut)
+export(pracSig13)
+export(reportRank)
+export(shapleyvalue)
+importFrom(NNS,NNS.boost)
+importFrom(NNS,dy.d_)
+importFrom(generalCorr,depMeas)
+importFrom(generalCorr,kern)
+importFrom(generalCorr,kern2)
+importFrom(generalCorr,parcorVec)
+importFrom(np,npreg)
+importFrom(np,npregbw)
+importFrom(randomForest,importance)
+importFrom(randomForest,randomForest)
+importFrom(stats,coef)
+importFrom(stats,cor)
+importFrom(stats,lm)
+importFrom(stats,median)
+importFrom(stats,residuals)
+importFrom(stats,var)
+importFrom(utils,combn)
+importFrom(xtable,xtable)
diff --git a/R/effSizCut.R b/R/effSizCut.R
new file mode 100644
index 0000000..7f3021f
--- /dev/null
+++ b/R/effSizCut.R
@@ -0,0 +1,84 @@
+#' Compute Effect Sizes for continuous or categorical data
+#'
+#' Psychologists' so-called "effect size" reveals
+#' the practical significance of only one
+#' regressor. This function generalizes their algorithm
+#' to two or more regressors (p>2). Generalization first
+#' converts the xi regressor into a categorical treatment variable
+#' with only two categories. One imagines that observations
+#' larger than the
+#' median (xit> median(xi)) are "treated," and those
+#' below the median are "untreated."
+#' The aim is the measure the size of the
+#' (treatment) effect of (xi) on y. Denote other variables
+#' with postscript "o" as (xo). Since we have p regressors in
+#' our multiple regression, we need to remove the nonlinear
+#' kernel regression effect of
+#' other variables (xo) on y while focusing on the effect of xi.
+#' There are two options in treating (xo) (i) letting xo be
+#' as they are in the data (ii) converting xo to binary
+#' at the median. One chooses the first option (i) by setting the
+#' logical argument ane=TRUE in calling the function.
+#' ane=TRUE is the default. Set ane=FALSE for the second option.
+#'
+#' @param y { (T x 1) vector of dependent variable data values}
+#' @param bigx { (T x p) data matrix of xi regressor variables associated
+#'  with the regression}
+#' @param ane {logical variable controls the treatment of other regressors.
+#'  If ane=TRUE (default), other regressors are used in kernel regression
+#'  without forcing them to be binary variables. When ane=FALSE,
+#'  the kernel regression removes the effect of other regressors
+#'  when other regressors are also binary type categorical variables,}
+#' @return out vector with p values of t-statistics for p regressors
+#' @note The aim is to answer the following question.
+#' Which regressor has the largest
+#' effect on the dependent variable? We assume that the signs
+#' of regressors are already adjusted such that a numerically
+#' larger effect size suggests that the corresponding regressor
+#' is most important, having the largest effect size in explaining
+#' y the dependent variable.
+#' @author Prof. H. D. Vinod, Economics Dept., Fordham University, NY
+#' @seealso \code{\link{pracSig13}}
+#' @importFrom generalCorr kern
+#' @examples
+#' set.seed(9)
+#'  y=sample(1:15,replace = TRUE)
+#'  x1=sample(2:16, replace = TRUE)
+#'  x2=sample(3:17, replace = TRUE)
+#' effSizCut(y,bigx=cbind(x1,x2),ane=TRUE)
+#'
+#' @export
+
+
+effSizCut=function(y,bigx, ane=TRUE){  #get t-stats
+  p=NCOL(bigx)
+  out=rep(NA,p)
+bigx2=apply(bigx,2,fncut)
+logi=apply(bigx2,2,as.logical)
+  for ( i in 1:p){
+  kxi=generalCorr::kern(dep.y=y, reg.x=bigx2[,i],residuals=TRUE)#r=resid
+  rxi=residuals(kxi)#resid= (y - yhat) so yhat=(y-resid)
+  xihat=y-rxi
+if(!ane){kother=generalCorr::kern(dep.y=y, reg.x=bigx[,-i],residuals=TRUE)
+  rother=residuals(kother)
+  xotherhat=y-rother }
+if(ane) xotherhat=rep(mean(y,na.rm=TRUE),length(y))
+  mylogi=logi[,i]
+  myxi=xihat[mylogi]
+  xibar=mean(myxi,na.rm=TRUE)
+  xivar=var(myxi,na.rm=TRUE)
+  myxo=xotherhat[mylogi]
+  xobar=mean(myxo,na.rm=TRUE) #o=other
+  xovar=var(myxo,na.rm=TRUE)
+  tim1=length(myxi)-1  #Ti-1, where ti=Ti, m1=minus 1
+#  print(c("i,Ti-1= ",i,tim1))
+  if(xivar<10e-9) denom=1 #var=variance
+  if(xovar<10e-9) denom=1
+#  if(denom==1) print(c("t-stat=NA, zero var. col.No.",i))
+  if(denom !=1) denom=sqrt(xivar/tim1+xovar/tim1)
+  #print(xivar,xovar)
+
+  out[i]=(xibar-xobar)/denom  }#end for loop
+  return(out)
+}
+
diff --git a/R/fncut.R b/R/fncut.R
new file mode 100644
index 0000000..cbed837
--- /dev/null
+++ b/R/fncut.R
@@ -0,0 +1,23 @@
+#'  fncut auxiliary converts continuous data into two categories
+#'
+#'
+#'  This is an internal function of the R package practicalSigni
+#'  Psychologists use effect size to evaluate the practical
+#'  importance of a treatment on a dependent variable using
+#'  a binary [0,1] variable.  Assuming numerical data, we
+#'  can always compute the median and regard values < or = the
+#'  median as zero and other values as unity.
+#'
+#' @param x {numerical vector of data values}
+#' @return x {vector of zeros and ones split at the median}
+#' @importFrom stats median
+#' @author Prof. H. D. Vinod, Fordham University, NY
+#'
+#'
+#' @export
+
+fncut=function(x){
+  mix=median(x)
+  x[x<=mix]=0
+  x[x>mix]=1
+  return(x)}
diff --git a/R/pracSig13.R b/R/pracSig13.R
new file mode 100644
index 0000000..7f6195d
--- /dev/null
+++ b/R/pracSig13.R
@@ -0,0 +1,240 @@
+#' Compute thirteen measures of practical significance
+#'
+#' Thirteen methods are denoted m1 to m13. Each
+#' yields p numbers when there are p regressors denoted xi.
+#' m1=OLS coefficient slopes. m2= t-stat of each slope.
+#' m3= beta coefficients OLS after all variables have
+#' mean zero and sd=1.
+#' m4= Pearson correlation coefficient between y and xi (only two
+#' variables at a time, assuming linearity).
+#' Let r*(y|xi) denote the generalized correlation coefficient allowing
+#' for nonlinearity from Vinod (2021, 2022). It does not equal
+#' analogous r*(xi|y). The larger of the two,
+#' max(r*(y|xi), r*(xi|y)), is given by
+#' the function depMeas() from the 'generalCorr' package.
+#' m5= depMeas, which allows nonlinearity. m5 is not comprehensive
+#' because it measures only two  variables, y and xi, at a time.
+#' m6= generalized partial correlation coefficient or
+#' GPCC. This is the first comprehensive measure
+#' of practical significance.
+#' m7=a generalization of psychologists' "effect size" after incorporating
+#' the nonlinear effect of other variables.
+#' m8= local linear partial (dy/dxi) using the 'np' package
+#' for kernel regressions and local linear derivatives.
+#' m9= partial derivative (dy/dxi) using the 'NNS' package.
+#' m10=importance measure using NNS.boost() function of 'NNS.'
+#' m11=Shapley Value measure of importance (cooperative game theory).
+#' m12 and m13= two versions of the random forest algorithm
+#' measuring the importance of regressors.
+#'
+#' If m6, m10 slow down computations, we recommend setting
+#' yes13[6]=0=yes13[10] to turn of computation of m6 and m10
+#' .
+#' @param y {input dependent variable data as a vector}
+#' @param bigx {input matrix of p regressor variables}
+#' @param yes13 {default vector of ones to compute all 13 measures.
+#' e.g., yes13[10]=0 means do not compute the m10 method.}
+#' @param verbo {logical to print results along the way default=FALSE}
+#' @return output matrix (p x 13) containing m1 to m13
+#' criteria (numerical measures of practical significance)
+#' along columns
+#' and a row for each regressor (excluding the intercept).
+#' @note needs the function kern(), which requires package 'np'.
+#' also needs 'NNS', 'randomForest', packages.
+#' @author Prof. H. D. Vinod, Economics Dept., Fordham University, NY
+#' @seealso See Also as \code{\link{effSizCut}},
+#' @seealso See Also as \code{\link{reportRank}}
+#' @importFrom stats coef cor lm median
+#' @importFrom stats  residuals var
+#' @importFrom utils combn
+#' @importFrom generalCorr depMeas parcorVec kern kern2
+#' @importFrom NNS dy.d_ NNS.boost
+#' @importFrom randomForest randomForest importance
+#' @importFrom np npreg npregbw
+#' @importFrom xtable xtable
+#' @references Vinod, H. D."Generalized Correlation and Kernel Causality with
+#'  Applications in Development Economics" in Communications in
+#'  Statistics -Simulation and Computation, 2015,
+#'  \doi{10.1080/03610918.2015.1122048}
+#' @keywords GPCC
+#' @keywords Shapley Value
+#' @keywords Random Forest
+#' @note The machine learning methods are subject to random seeds.
+#' For some seed values, m10 values from NNS.boost() become
+#' degenerate and are reported as NA or missing. In that case
+#' the average ranking output r613 from reportRank() needs manual adjustments.
+#'
+#' @references Vinod, H. D.", "Generalized Correlations and Instantaneous Causality
+#'  for Data Pairs Benchmark," (March 8, 2015).
+#'   \url{https://www.ssrn.com/abstract=2574891}
+#' @references Vinod, H. D. “Generalized, Partial and Canonical
+#' Correlation Coefficients,” Computational Economics
+#' (2021) SpringerLink vol. 59, pp.1-28.
+#' URL https://link.springer.com/article/10.1007/s10614-021-10190-x
+#' @references Vinod, H. D. “Kernel regression coefficients for practical
+#' significance," Journal of Risk and Financial Management
+#' 15(1), 2022 pp.1-13. https://doi.org/10.3390/jrfm15010032
+#' @references Vinod, H. D.", "Hands-On Intermediate Econometrics
+#' Using R"  (2022) World Scientific Publishers: Hackensack, NJ.
+#' \url{https://www.worldscientific.com/worldscibooks/10.1142/12831}
+#'
+#' @export
+
+
+
+pracSig13=function(y,bigx, yes13=rep(1,13),verbo=FALSE){
+  if (verbo){
+  print("m1=ols coeff values")
+  print("Linear regression focusing of statistical significance")}
+  importance=NULL
+  p=NCOL(bigx)
+  mall=rep(NA,13)
+  for(i in 1:13) mall[i]=paste("m",i,sep="")
+  for(i in 1:13) assign(mall[i],rep(NA,p))
+  if(verbo) print(c("initialize typical mi, m4=", m4))
+  myp=1:p
+  nam=colnames(bigx)
+  inam=as.character(1:p);inam
+  if(length(nam)==0) nam=paste("x",eval(myp),sep="")
+  if(verbo)  print(nam)
+  reg1=lm(y ~ bigx)
+  if (verbo){
+    print("compute m1=OLS coeff. values using original data")}
+  if (yes13[1]==1){
+  m1=coef(reg1)[2:(p+1)]} #omit intercept coeff magnitudes
+  if (verbo){
+    print(m1)}
+
+  if (verbo){
+    print("compute m2=t-stat values")}
+  stu1x=summary(reg1)$coef[,3] #third col=t-values
+  if (yes13[2]==1){
+    m2=stu1x[2:(p+1)]
+  if (verbo) print(m2)}
+
+  if (verbo){
+    print("m3=OLS regression coefficients on standardized data")}
+  if (yes13[3]==1){
+  sy=scale(y)
+  sbigx=apply(bigx,2,scale) #data are standardized
+  if (verbo){
+  print("regression forced through origin, so NO intercept")}
+  reg2=lm(sy~sbigx-1); m3=coef(reg2)
+  if (verbo){
+  print("standardized regr coefficients=practical importance beta")
+  print(m3)}
+  } #endif yes13[3]
+
+  if (yes13[4]==1){
+    m4=rep(NA,p)
+  if (verbo){
+    print("Pearson corr is m4")}
+  for (i in 1:p) m4[i]=cor(y,bigx[,i])
+  if (verbo){
+    print(m4)}
+  } # endif yes13[5]
+
+  if (yes13[5]==1){
+    m5=rep(NA,p)
+  if (verbo){
+    print("generalized corr max(r*ij, r*ji) is m5")}
+  for (i in 1:p) m5[i]=generalCorr::depMeas(y,bigx[,i])
+  if (verbo){
+    print(m5)}
+  }
+
+  if (yes13[6]==1){
+    if (verbo){
+    print("m6=generalized partial correl. coeff")}
+  m6=as.vector(generalCorr::parcorVec(cbind(y,bigx)))
+  if (verbo){
+    print(m6)}}
+
+  if (yes13[7]==1){
+  if (verbo){
+    print("m7=effect size Psychology, force xi=(0,1) at median")}
+  m7=effSizCut(y,bigx,ane=TRUE)
+  if (verbo){
+    print(m7)} }
+
+  if (yes13[8]==1){
+    if (verbo){
+    print("now m8 for local linear partials dy/dxi using np package")}
+  options(np.messages=FALSE)
+  scaly=as.vector(scale(y))
+  sbigx=apply(bigx,2,scale)
+  m8=rep(NA,p) #place to store
+  for ( i in 1:p){
+    kxi=generalCorr::kern2(dep.y=y, reg.x=sbigx[,i],gradients=TRUE)
+    m8[i]=apply(kxi$grad,2,mean) }
+  if (verbo){
+    print("np estimate of mean of derivatives standardized units")
+  print(round(m8,3))}
+} #endif m8 calculations
+
+  if (yes13[9]==1){
+    if (verbo){
+    print("now m9 dy/dxi using NNS package")}
+  scaly=scale(y)
+  sbigx=apply(bigx,2,scale)
+  nnsout=NNS::dy.d_(x=sbigx, y=scaly[,1], wrt=1:p,
+               messages=FALSE, eval.points = "obs")
+  lapply(nnsout["First",], mean)
+  m9x= lapply(nnsout["First",], mean)
+  m9=as.numeric(m9x)
+  if (verbo){
+    print(m9)}
+} #endif m9 calculations
+
+  if (yes13[10]==1 & verbo) {
+    print("now m10 boost importance using NNS package")}
+
+    if (yes13[10]==1){
+  NNSb=NNS::NNS.boost(IVs.train = bigx, DV.train = y,status=FALSE,
+                 feature.importance = FALSE)
+  m10=as.numeric(NNSb$feature.weights)
+  if(verbo)print(m10)
+  }# endif m10
+
+  if (yes13[11]==1){
+    if (verbo){
+    print("now m11 Shapley measure of importance")}
+    bigxx=data.frame(bigx)
+  m11=as.numeric(shapleyvalue(y,bigxx)[2,])
+  if (verbo)   print(m11)
+  } # endif m11
+
+  if (yes13[12]==1 | yes13[13]==1){
+  if (verbo){
+  print("now m12, m13 random forest measures of importance")}
+  y.rf = randomForest(x=bigx,y=y, importance=TRUE)
+  imp12=round(importance(y.rf), 3)
+  if(yes13[12]==1) m12=as.numeric(imp12[,1])
+  if (verbo){
+    print(m12)}
+  if(yes13[13]==1) m13=as.numeric(imp12[,2])
+  if (verbo){
+    print(m13)}
+} #endif m12 m13
+
+  out=matrix(NA,nrow=p, ncol=13)
+
+  for (j in 1:13){
+  if (j==1 & yes13[j]==1)  out[,j]=m1
+  if (j==2 & yes13[j]==1)  out[,j]=m2
+  if (j==3 & yes13[j]==1)  out[,j]=m3
+  if (j==4 & yes13[j]==1)  out[,j]=m4
+  if (j==5 & yes13[j]==1)  out[,j]=m5
+  if (j==6 & yes13[j]==1)  out[,j]=m6
+  if (j==7 & yes13[j]==1)  out[,j]=m7
+  if (j==8 & yes13[j]==1)  out[,j]=m8
+  if (j==9 & yes13[j]==1)  out[,j]=m9
+  if(length(m10)==p){
+  if (j==10 & yes13[j]==1)  out[,j]=m10}
+  if (j==11 & yes13[j]==1)  out[,j]=m11
+  if (j==12 & yes13[j]==1)  out[,j]=m12
+  if (j==13 & yes13[j]==1)  out[,j]=m13
+  }
+  rownames(out)=nam
+  return(out)
+}
diff --git a/R/reportRank.R b/R/reportRank.R
new file mode 100644
index 0000000..32c5e80
--- /dev/null
+++ b/R/reportRank.R
@@ -0,0 +1,134 @@
+#' Function to report ranks of 13 criteria for practical significance
+#'
+#' This function generates a report based on the
+#' regression of y on bigx.
+#' It acknowledges that some methods for evaluating the importance
+#' of  regressor in explaining y may give the importance value
+#' with a wrong (unrealistic) sign. For example, m2 reports t-values. Imagine
+#' that due to collinearity m2 value is negative when the correct
+#' sign from prior knowledge of the subject matter is that the
+#' coefficient should be positive, and hence the t-stat should be positive.
+#' The wrong sign means the importance of regressor in explaining y
+#' should be regarded as relatively less important. The larger the
+#' absolute size of the t-stat, the less its true importance in
+#' measuring y. The ranking of coefficients computed here
+#' suitably deprecates the importance of the regressor
+#' when its coefficient has the wrong sign (perverse
+#' direction).
+#'
+#'
+#'
+#' @param y { (T x 1) vector of dependent variable data values}
+#' @param bigx { (T x p) data marix of xi regressor variables associated
+#'  with the regression}
+#' @param yesLatex {default 1 means print Latex-ready Tables}
+#' @param yes13 {default vector of ones to compute all 13 measures.}
+#' @param bsign {A (p x 1) vector of right signs of regression
+#' coefficients. Default is bsign=0 means the right sign is the same
+#' as the sign of the covariance, cov(y, xi) }
+#' @param dig {digits to be printed in latex tables, default, dig=d33}
+#' @param verbo {logical to print results by pracSig13, default=FALSE}
+#' @author Prof. H. D. Vinod, Economics Dept., Fordham University, NY
+#' @importFrom stats coef cor lm median residuals var
+#' @seealso \code{\link{pracSig13}}
+#' @note The machine learning methods are subject to random seeds.
+#' For some seed values, m10 values from NNS.boost() rarely become
+#' degenerate and are reported as NA or missing. In that case
+#' the average ranking output r613 here needs adjustment.
+#' @return
+#'  \item{v15}{practical significance index values (sign adjusted)
+#'  for m1 to m5 using older linear and /or bivariate methods}
+#'  \item{v613}{practical significance index values
+#'  for m6 to m13 newer
+#'  comprehensive and nonlinear methods}
+#'  \item{r15}{ranks and average rank for m1 to m5
+#'  using older linear and /or bivariate methods}
+#'  \item{r613}{ranks and average rank for m6 to m13 newer
+#'  comprehensive and nonlinear methods}
+#'
+#'
+#' @examples
+#' \donttest{
+#' set.seed(9)
+#' y=sample(1:15,replace = TRUE)
+#' x1=sample(2:16, replace = TRUE)
+#' x2=sample(3:17, replace = TRUE)
+#' x3=sample(4:18,replace = TRUE)
+#' reportRank(y,bigx=cbind(x1,x2,x3))
+#' }
+#'
+#' @export
+
+
+reportRank=function(y, bigx, yesLatex=1, yes13=rep(1,13),
+                    bsign=0,dig=3,verbo=FALSE) {
+  d3=dig
+  p=NCOL(bigx)
+  m1=NULL;m2=NULL;m3=NULL;m4=NULL;m5=NULL;m6=NULL;m7=NULL
+  m8=NULL;m9=NULL;m10=NULL;m11=NULL;m12=NULL;m13=NULL
+  nam=colnames(bigx)
+  if (bsign==0) {
+    bsign=rep(1,p)
+    for (i in 1:p) bsign[i]=sign(cor(y,bigx[,i])) }
+  p13=pracSig13(y,bigx,verbo=verbo,yes13=yes13)
+  if(verbo){
+  print("values before sign adjustment for m1 to m9")
+  print("Sign always positive for importance indexes m10 t0 m13")
+  print(p13[,1:6],digits=d3)
+  print(p13[,7:13],digits=d3)}
+  if(verbo){
+  if(yesLatex==1){
+  print("values before sign adjustment for m1 to m9")
+    print(xtable::xtable(p13[,1:6],digits=d3))
+    print(xtable::xtable(p13[,7:13],digits=d3)) } }
+  sg19=diag(bsign)%*%p13[,1:9]
+  PSM=cbind(sg19,p13[,10:13])
+  colnames(PSM)=c("m1","m2","m3","m4","m5","m6",
+              "m7","m8","m9","m10","m11","m12","m13")
+  if(verbo){
+    print("values AFTER sign adjustment for m1 to m9")
+  print(PSM[,1:6],digits=d3)
+  print(PSM[,7:13],digits=d3)
+  if(yesLatex==1){
+    print("values before sign adjustment for m1 to m9")
+  print(xtable::xtable(PSM[,1:6],digits=d3))
+  print(xtable::xtable(PSM[,7:13],digits=d3)) }}
+n13=NCOL(PSM)
+for(j in 1:13) assign(paste("m",j,sep=""),PSM[,j])
+#attach(data.frame(PSM))
+if(verbo) print(nam)
+v15=cbind(m1,m2,m3,m4,m5)  #v=values 15=m1 to m5
+rownames(v15)=nam
+if(verbo){
+print("values of m1 to m5 after sign adjustments if any")
+print(v15)
+if(verbo){
+if(yesLatex==1) print(xtable::xtable(v15,digits=4))
+  print("above v15 values of m1 to m5")}}
+#now compute rank numbers for m1 to m5
+nr=NROW(v15)
+rank15=((nr+1)-apply(v15,2,rank,na.last=TRUE, ties.method="average"))
+if(verbo) print("ranks values as they are")
+avrank15=round(apply(rank15,1,mean),1)
+r15=cbind(rank15,avrank15)
+if(verbo) {print(r15)
+if(yesLatex==1) print(xtable::xtable(r15,digits=2))}
+v613=cbind(m6,m7,m8,m9,m10,m11,m12,m13)
+#detach(data.frame(PSM))
+rownames(v613)=nam
+if(verbo) {print(v613)
+if(yesLatex==1) print(xtable::xtable(v613,digits=4))}
+nr=NROW(v613)
+rank613=((nr+1)-apply(v613,2,rank,na.last=TRUE,
+                      ties.method="average"))
+if(verbo){
+print("sign adjusted (if necessary) ranks of p regressors")}
+avrank613=round(apply(rank613,1,mean),1)
+r613=cbind(rank613,avrank613)
+if(verbo){
+print(r613)
+if(yesLatex==1) print(xtable::xtable(r613,digits=2))
+}
+list(v15=v15, v613=v613, r15=r15, r613=r613)
+}
+
diff --git a/R/shapleyvalue.R b/R/shapleyvalue.R
new file mode 100644
index 0000000..c1d67bc
--- /dev/null
+++ b/R/shapleyvalue.R
@@ -0,0 +1,140 @@
+#' Shapley Value Regression importance of attributes in linear regression
+#'
+#' 'ShapleyValue' package function shapleyvalue()
+#' calculates the relative importance
+#' of independent variables in linear regression while avoiding
+#' collinearity. This function is a minor modification of the function
+#' in the package ShapleyValue. The original function fails for me when
+#' a variable name is x1. Since Jingyi Liang,
+#' the maintainer of 'ShapleyValue,' has ignored several requests
+#' to fix the error, this function
+#' is reluctantly included here as a separate
+#' function.
+#' @param y {input dependent variable data as a vector}
+#' @param x {input matrix of p regressor variables must be a data frame}
+#' @return The structure of the output is a datatable, with two rows:
+#' the unstandardized and standardized relative importance of each
+#' attributes using shapley value regression method.
+#' @author H. D. Vinod, Fordham University, NY
+#' @importFrom stats lm
+#' @references Liang J (2021). ShapleyValue: Shapley Value Regression for
+#' Relative Importance of Attributes. R package version 0.2.0,
+#' \url{https://CRAN.R-project.org/package=ShapleyValue}.
+#'
+#' @export
+
+
+shapleyvalue=
+  function (y, x)
+  {
+    k <- ncol(x)
+    times_wt <- NULL
+    for (i in 1:k - 1) {
+      times_wt[i] <- choose(k - 1, i)
+    }
+    data <- data.frame(matrix(ncol = k, nrow = 2^(k - 1)))
+    name <- colnames(x)
+    if (k == 1) {
+      stop(simpleError("No need for using shapley regression!"))
+    }
+    else if (k == 2) {
+      for (i in 1:k) {
+        n <- name[i]
+        lm <- lm(y ~ x[, n])
+        lm2 <- lm(y ~ x[, -i])
+        lm_all <- lm(y ~ ., data = x)
+        r1 <- summary(lm)$r.squared
+        r2 <- summary(lm2)$r.squared
+        r3 <- summary(lm_all)$r.squared
+        data[1, i] <- r1
+        data[2, i] <- r3 - r2
+      }
+      weight <- c(1/2, 1/2)
+      data <- as.matrix(data)
+      dat <- weight %*% data
+      dat <- as.data.frame(dat)
+      sum <- rowSums(dat)
+      uni_value <- dat/sum
+      dat <- rbind(round(dat, 4), round(uni_value, 4))
+      colnames(dat) <- name
+      rownames(dat) <- c("Shapley Value", "Standardized Shapley Value")
+      return(dat)
+    }
+    else {
+      for (i in 1:k) {
+        num <- 1
+        for (j in 1:k) {
+          if (j == 1) {
+            n <- name[i]
+            lm <- lm(y ~ x[, n])
+            data[1, i] <- summary(lm)$r.squared
+          }
+          else if (j == k) {
+            xx <- combn(k, k)
+            xlab <- NULL
+            for (l in 1:j) {
+              x_lab[l] <- xx[l]
+              if (x_lab[l] == i) {
+                x1dummy <- x[, x_lab[l]]
+                x_reslab <- xx[-l]
+                x_res <- x[x_reslab]
+                x_res <- cbind(x_res, x1dummy)
+                lm <- lm(y ~ . - x1dummy, data = x_res)
+                lm2 <- lm(y ~ ., data = x_res)
+                r <- summary(lm)$r.squared
+                r2 <- summary(lm2)$r.squared
+              }
+            }
+            data[2^(k - 1), i] <- r2 - r
+          }
+          else {
+            times <- choose(k - 1, j - 1)
+            times2 <- choose(k, j)
+            combine <- combn(k, j)
+            n <- NULL
+            nn <- combine
+            for (m in 1:j) {
+              nn[m, ] <- combine[m, ] == i
+            }
+            for (m in 1:times2) {
+              n[m] <- 1 %in% nn[, m]
+            }
+            xx <- combine[, which(n)]
+            for (o in 1:times) {
+              num <- num + 1
+              x_lab <- NULL
+              for (l in 1:j) {
+                x_lab[l] <- xx[, o][l]
+                if (x_lab[l] == i) {
+                  x1dummy <- x[, x_lab[l]]
+                  x_reslab <- xx[, o][-l]
+                  x_res <- x[x_reslab]
+                  x_res <- cbind(x_res, x1dummy)
+                  lm <- lm(y ~ . - x1dummy, data = x_res)
+                  lm2 <- lm(y ~ ., data = x_res)
+                  r <- summary(lm)$r.squared
+                  r2 <- summary(lm2)$r.squared
+                }
+              }
+              data[num, i] <- r2 - r
+            }
+          }
+        }
+      }
+      weight <- 1/k
+      for (q in 1:k - 1) {
+        weight <- c(weight, rep(1/k/times_wt[q], times_wt[q]))
+      }
+      data <- as.matrix(data)
+      weight <- as.matrix(weight)
+      weight <- t(weight)
+      dat2 <- weight %*% data
+      dat2 <- as.data.frame(dat2)
+      sum <- rowSums(dat2)
+      uni_value <- dat2/sum
+      dat2 <- rbind(round(dat2, 4), round(uni_value, 4))
+      colnames(dat2) <- name
+      rownames(dat2) <- c("Shapley Value", "Standardized Shapley Value")
+      return(dat2)
+    }
+  }
diff --git a/build/partial.rdb b/build/partial.rdb
new file mode 100644
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diff --git a/build/vignette.rds b/build/vignette.rds
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diff --git a/inst/doc/practicalSigni-vignette.pdf b/inst/doc/practicalSigni-vignette.pdf
new file mode 100644
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diff --git a/inst/doc/practicalSigni-vignette.pdf.asis b/inst/doc/practicalSigni-vignette.pdf.asis
new file mode 100644
index 0000000..a099b2a
--- /dev/null
+++ b/inst/doc/practicalSigni-vignette.pdf.asis
@@ -0,0 +1,6 @@
+%\VignetteIndexEntry{practicalSigni-vignette}
+%\VignetteEngine{R.rsp::asis}
+%\VignetteKeyword{PDF}
+%\VignetteKeyword{HTML}
+%\VignetteKeyword{vignette}
+%\VignetteKeyword{package}
diff --git a/man/effSizCut.Rd b/man/effSizCut.Rd
new file mode 100644
index 0000000..19b7dc0
--- /dev/null
+++ b/man/effSizCut.Rd
@@ -0,0 +1,68 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/effSizCut.R
+\name{effSizCut}
+\alias{effSizCut}
+\title{Compute Effect Sizes for continuous or categorical data}
+\usage{
+effSizCut(y, bigx, ane = TRUE)
+}
+\arguments{
+\item{y}{{ (T x 1) vector of dependent variable data values}}
+
+\item{bigx}{{ (T x p) data matrix of xi regressor variables associated
+with the regression}}
+
+\item{ane}{{logical variable controls the treatment of other regressors.
+If ane=TRUE (default), other regressors are used in kernel regression
+without forcing them to be binary variables. When ane=FALSE,
+the kernel regression removes the effect of other regressors
+when other regressors are also binary type categorical variables,}}
+}
+\value{
+out vector with p values of t-statistics for p regressors
+}
+\description{
+Psychologists' so-called "effect size" reveals
+the practical significance of only one
+regressor. This function generalizes their algorithm
+to two or more regressors (p>2). Generalization first
+converts the xi regressor into a categorical treatment variable
+with only two categories. One imagines that observations
+larger than the
+median (xit> median(xi)) are "treated," and those
+below the median are "untreated."
+The aim is the measure the size of the
+(treatment) effect of (xi) on y. Denote other variables
+with postscript "o" as (xo). Since we have p regressors in
+our multiple regression, we need to remove the nonlinear
+kernel regression effect of
+other variables (xo) on y while focusing on the effect of xi.
+There are two options in treating (xo) (i) letting xo be
+as they are in the data (ii) converting xo to binary
+at the median. One chooses the first option (i) by setting the
+logical argument ane=TRUE in calling the function.
+ane=TRUE is the default. Set ane=FALSE for the second option.
+}
+\note{
+The aim is to answer the following question.
+Which regressor has the largest
+effect on the dependent variable? We assume that the signs
+of regressors are already adjusted such that a numerically
+larger effect size suggests that the corresponding regressor
+is most important, having the largest effect size in explaining
+y the dependent variable.
+}
+\examples{
+set.seed(9)
+ y=sample(1:15,replace = TRUE)
+ x1=sample(2:16, replace = TRUE)
+ x2=sample(3:17, replace = TRUE)
+effSizCut(y,bigx=cbind(x1,x2),ane=TRUE)
+
+}
+\seealso{
+\code{\link{pracSig13}}
+}
+\author{
+Prof. H. D. Vinod, Economics Dept., Fordham University, NY
+}
diff --git a/man/fncut.Rd b/man/fncut.Rd
new file mode 100644
index 0000000..3712f3e
--- /dev/null
+++ b/man/fncut.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/fncut.R
+\name{fncut}
+\alias{fncut}
+\title{fncut auxiliary converts continuous data into two categories}
+\usage{
+fncut(x)
+}
+\arguments{
+\item{x}{{numerical vector of data values}}
+}
+\value{
+x {vector of zeros and ones split at the median}
+}
+\description{
+This is an internal function of the R package practicalSigni
+ Psychologists use effect size to evaluate the practical
+ importance of a treatment on a dependent variable using
+ a binary [0,1] variable.  Assuming numerical data, we
+ can always compute the median and regard values < or = the
+ median as zero and other values as unity.
+}
+\author{
+Prof. H. D. Vinod, Fordham University, NY
+}
diff --git a/man/pracSig13.Rd b/man/pracSig13.Rd
new file mode 100644
index 0000000..f21f0a2
--- /dev/null
+++ b/man/pracSig13.Rd
@@ -0,0 +1,102 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/pracSig13.R
+\name{pracSig13}
+\alias{pracSig13}
+\title{Compute thirteen measures of practical significance}
+\usage{
+pracSig13(y, bigx, yes13 = rep(1, 13), verbo = FALSE)
+}
+\arguments{
+\item{y}{{input dependent variable data as a vector}}
+
+\item{bigx}{{input matrix of p regressor variables}}
+
+\item{yes13}{{default vector of ones to compute all 13 measures.
+e.g., yes13[10]=0 means do not compute the m10 method.}}
+
+\item{verbo}{{logical to print results along the way default=FALSE}}
+}
+\value{
+output matrix (p x 13) containing m1 to m13
+criteria (numerical measures of practical significance)
+along columns
+and a row for each regressor (excluding the intercept).
+}
+\description{
+Thirteen methods are denoted m1 to m13. Each
+yields p numbers when there are p regressors denoted xi.
+m1=OLS coefficient slopes. m2= t-stat of each slope.
+m3= beta coefficients OLS after all variables have
+mean zero and sd=1.
+m4= Pearson correlation coefficient between y and xi (only two
+variables at a time, assuming linearity).
+Let r*(y|xi) denote the generalized correlation coefficient allowing
+for nonlinearity from Vinod (2021, 2022). It does not equal
+analogous r*(xi|y). The larger of the two,
+max(r*(y|xi), r*(xi|y)), is given by
+the function depMeas() from the 'generalCorr' package.
+m5= depMeas, which allows nonlinearity. m5 is not comprehensive
+because it measures only two  variables, y and xi, at a time.
+m6= generalized partial correlation coefficient or
+GPCC. This is the first comprehensive measure
+of practical significance.
+m7=a generalization of psychologists' "effect size" after incorporating
+the nonlinear effect of other variables.
+m8= local linear partial (dy/dxi) using the 'np' package
+for kernel regressions and local linear derivatives.
+m9= partial derivative (dy/dxi) using the 'NNS' package.
+m10=importance measure using NNS.boost() function of 'NNS.'
+m11=Shapley Value measure of importance (cooperative game theory).
+m12 and m13= two versions of the random forest algorithm
+measuring the importance of regressors.
+}
+\details{
+If m6, m10 slow down computations, we recommend setting
+yes13[6]=0=yes13[10] to turn of computation of m6 and m10
+.
+}
+\note{
+needs the function kern(), which requires package 'np'.
+also needs 'NNS', 'randomForest', packages.
+
+The machine learning methods are subject to random seeds.
+For some seed values, m10 values from NNS.boost() become
+degenerate and are reported as NA or missing. In that case
+the average ranking output r613 from reportRank() needs manual adjustments.
+}
+\references{
+Vinod, H. D."Generalized Correlation and Kernel Causality with
+ Applications in Development Economics" in Communications in
+ Statistics -Simulation and Computation, 2015,
+ \doi{10.1080/03610918.2015.1122048}
+
+Vinod, H. D.", "Generalized Correlations and Instantaneous Causality
+ for Data Pairs Benchmark," (March 8, 2015).
+  \url{https://www.ssrn.com/abstract=2574891}
+
+Vinod, H. D. “Generalized, Partial and Canonical
+Correlation Coefficients,” Computational Economics
+(2021) SpringerLink vol. 59, pp.1-28.
+URL https://link.springer.com/article/10.1007/s10614-021-10190-x
+
+Vinod, H. D. “Kernel regression coefficients for practical
+significance," Journal of Risk and Financial Management
+15(1), 2022 pp.1-13. https://doi.org/10.3390/jrfm15010032
+
+Vinod, H. D.", "Hands-On Intermediate Econometrics
+Using R"  (2022) World Scientific Publishers: Hackensack, NJ.
+\url{https://www.worldscientific.com/worldscibooks/10.1142/12831}
+}
+\seealso{
+See Also as \code{\link{effSizCut}},
+
+See Also as \code{\link{reportRank}}
+}
+\author{
+Prof. H. D. Vinod, Economics Dept., Fordham University, NY
+}
+\keyword{Forest}
+\keyword{GPCC}
+\keyword{Random}
+\keyword{Shapley}
+\keyword{Value}
diff --git a/man/reportRank.Rd b/man/reportRank.Rd
new file mode 100644
index 0000000..e9af3d9
--- /dev/null
+++ b/man/reportRank.Rd
@@ -0,0 +1,85 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/reportRank.R
+\name{reportRank}
+\alias{reportRank}
+\title{Function to report ranks of 13 criteria for practical significance}
+\usage{
+reportRank(
+  y,
+  bigx,
+  yesLatex = 1,
+  yes13 = rep(1, 13),
+  bsign = 0,
+  dig = 3,
+  verbo = FALSE
+)
+}
+\arguments{
+\item{y}{{ (T x 1) vector of dependent variable data values}}
+
+\item{bigx}{{ (T x p) data marix of xi regressor variables associated
+with the regression}}
+
+\item{yesLatex}{{default 1 means print Latex-ready Tables}}
+
+\item{yes13}{{default vector of ones to compute all 13 measures.}}
+
+\item{bsign}{{A (p x 1) vector of right signs of regression
+coefficients. Default is bsign=0 means the right sign is the same
+as the sign of the covariance, cov(y, xi) }}
+
+\item{dig}{{digits to be printed in latex tables, default, dig=d33}}
+
+\item{verbo}{{logical to print results by pracSig13, default=FALSE}}
+}
+\value{
+\item{v15}{practical significance index values (sign adjusted)
+ for m1 to m5 using older linear and /or bivariate methods}
+ \item{v613}{practical significance index values
+ for m6 to m13 newer
+ comprehensive and nonlinear methods}
+ \item{r15}{ranks and average rank for m1 to m5
+ using older linear and /or bivariate methods}
+ \item{r613}{ranks and average rank for m6 to m13 newer
+ comprehensive and nonlinear methods}
+}
+\description{
+This function generates a report based on the
+regression of y on bigx.
+It acknowledges that some methods for evaluating the importance
+of  regressor in explaining y may give the importance value
+with a wrong (unrealistic) sign. For example, m2 reports t-values. Imagine
+that due to collinearity m2 value is negative when the correct
+sign from prior knowledge of the subject matter is that the
+coefficient should be positive, and hence the t-stat should be positive.
+The wrong sign means the importance of regressor in explaining y
+should be regarded as relatively less important. The larger the
+absolute size of the t-stat, the less its true importance in
+measuring y. The ranking of coefficients computed here
+suitably deprecates the importance of the regressor
+when its coefficient has the wrong sign (perverse
+direction).
+}
+\note{
+The machine learning methods are subject to random seeds.
+For some seed values, m10 values from NNS.boost() rarely become
+degenerate and are reported as NA or missing. In that case
+the average ranking output r613 here needs adjustment.
+}
+\examples{
+\donttest{
+set.seed(9)
+y=sample(1:15,replace = TRUE)
+x1=sample(2:16, replace = TRUE)
+x2=sample(3:17, replace = TRUE)
+x3=sample(4:18,replace = TRUE)
+reportRank(y,bigx=cbind(x1,x2,x3))
+}
+
+}
+\seealso{
+\code{\link{pracSig13}}
+}
+\author{
+Prof. H. D. Vinod, Economics Dept., Fordham University, NY
+}
diff --git a/man/shapleyvalue.Rd b/man/shapleyvalue.Rd
new file mode 100644
index 0000000..f76d3e8
--- /dev/null
+++ b/man/shapleyvalue.Rd
@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/shapleyvalue.R
+\name{shapleyvalue}
+\alias{shapleyvalue}
+\title{Shapley Value Regression importance of attributes in linear regression}
+\usage{
+shapleyvalue(y, x)
+}
+\arguments{
+\item{y}{{input dependent variable data as a vector}}
+
+\item{x}{{input matrix of p regressor variables must be a data frame}}
+}
+\value{
+The structure of the output is a datatable, with two rows:
+the unstandardized and standardized relative importance of each
+attributes using shapley value regression method.
+}
+\description{
+'ShapleyValue' package function shapleyvalue()
+calculates the relative importance
+of independent variables in linear regression while avoiding
+collinearity. This function is a minor modification of the function
+in the package ShapleyValue. The original function fails for me when
+a variable name is x1. Since Jingyi Liang,
+the maintainer of 'ShapleyValue,' has ignored several requests
+to fix the error, this function
+is reluctantly included here as a separate
+function.
+}
+\references{
+Liang J (2021). ShapleyValue: Shapley Value Regression for
+Relative Importance of Attributes. R package version 0.2.0,
+\url{https://CRAN.R-project.org/package=ShapleyValue}.
+}
+\author{
+H. D. Vinod, Fordham University, NY
+}
diff --git a/vignettes/practicalSigni-vignette.pdf.asis b/vignettes/practicalSigni-vignette.pdf.asis
new file mode 100644
index 0000000..a099b2a
--- /dev/null
+++ b/vignettes/practicalSigni-vignette.pdf.asis
@@ -0,0 +1,6 @@
+%\VignetteIndexEntry{practicalSigni-vignette}
+%\VignetteEngine{R.rsp::asis}
+%\VignetteKeyword{PDF}
+%\VignetteKeyword{HTML}
+%\VignetteKeyword{vignette}
+%\VignetteKeyword{package}