R functions: http://www.agreestat.com/r_functions.html

#nominal
gwet.ac1.raw(myData)
myData1<-table(myData); kappa2.table(myData1)
#nominal 3 + raters
fleiss.kappa.raw(myData)
#ordinal
gwet.ac1.raw(myData, weights="ordinal")
#scale
ICC(myData)

source("https://kucera.lancs.ac.uk/stats/r_functions/loglik.r");
source("https://kucera.lancs.ac.uk/stats/r_functions/CramerV.r");
source("https://kucera.lancs.ac.uk/stats/r_functions/riskratio.r");
#cross-tabulate
data<- table(data)
#statistical tests
chisq.test(data, correct = FALSE);
chisq.test(data, correct = TRUE);
g.test(data, correct = "none");
fisher.test(data);
#effect sizes
CramerV(data, conf.level = 0.95);
riskratio(data);
 

library(Hmisc); library(corrplot); library(stats); #libraries used
cor.test(mydata1, mydata2, method="pearson") #Pearson's correlation
cor.test(mydata1, mydata2, method="spearman") #Spearman's correlation
rcorr(mydata, type="pearson") #correlation matrix
plot(mydata, col ="blue"); fitline <- lm(mydata1 ~ mydata2); abline(fitline,col="red") #scatter plot
corrplot(m, method ="color", type = "full",  diag = TRUE, addCoef.col="black", addCoefasPercent=FALSE, addgrid.col="grey", tl.pos = NULL, tl.cex = 1, tl.srt = 45, tl.col = "black") #correlation matrix
 

  mydata <- scale(mydata) # optional z-score transformation
d <- dist(mydata, method = "manhattan") # distance matrix
 fit <- hclust(d, method="ward.D") #Cluster analysis
 plot(fit, xlab="", ylab="Height", main="")#plot dendrogram
 rect.hclust(fit, k=5, border="red") #draw cluster groups

cortest.bartlett(mydata); det(cor(mydata))# Bartlett's test and multi-colinearity test
fa.parallel(mydata, fa="fa", main = "Scree Plot", show.legend=FALSE) #screeplot
factanal(mydata, number, rotation="promax") #factor analysis

#t-test
t.test(data[ ,1], data[ ,2], paired=FALSE)
#t-test: repeated measures
t.test(data[ ,1], data[ ,2], paired=TRUE)
#Mann-Whitney-wilcoxon rank sum test
wilcox.test(data[ ,1], data[ ,2], paired=FALSE)
#Mann-Whitney-Wilcoxon rank sum test: repeated measures
wilcox.test(data[ ,1], data[ ,2], paired=TRUE)
#One-way ANOVA
aov(measurement ~ group, data = data)
#Kruskal-Wallis test
kruskal.test(data)
 

library(languageR);
x = corres.fnc(data);
plot(x, ccex = 0.6, rcex = 0.6);
 

library(lme4);
    glmer(outcome~predictor+(1|randeffect), family = binomial, data = mydata);

library(boot);
source("http://users.ics.aalto.fi/lijffijt/bootstraptest/bootstraptest.R");
bootstraptest(period1, period2,samples,'p2');
percid(b);
boot(data=b, statistic=percid, R=samples);

 

source("https://kucera.lancs.ac.uk/stats/r_functions/VNC.r");
abc(data,"sd");
 

 library(ggplot2)
library(mgcv)
p<-ggplot(data, aes(x = data[,1], y =data[,2])) + geom_point()  + xladata("Time") + yladata("Linguistic variadatale"); p + stat_smooth(method = "gam", formula = y ~ s(x, datas = "cr", fx=FALSE, k =15), size = 1, fill="#707070", level = 0.95 )+ stat_smooth(method = "gam", formula = y ~ s(x, datas = "cr", fx=FALSE, k =15), size = 1, fill="#FFFF00", level = 0.99);
 

#Calculate Gwet's AC1; b...input data frame
i = 1; v <- c(); while(i+1 < ncol(b)) {n=(gwet.ac1.raw(b[,i:(i+2)])[3]);v<- c(v, n); i= i+1; }

#Prepare data frame
h<-seq(from, to, by = 1); g<-data.frame(h,v)

#Produce graph
p<-ggplot(g, aes(x = g[,1], y =g[,2])) + xlim(from, to)+ scale_x_continuous(breaks = seq(from, to, by = 10)) + geom_point()  + xlab("Time") + ylab("AC1"); p + stat_smooth(method = "gam", formula = y ~ s(x, bs = "cr", fx=FALSE, k =10), size = 1, fill="#707070", level = 0.95 )+ stat_smooth(method = "gam", formula = y ~ s(x, bs = "cr", fx=FALSE, k =10), size = 1, fill="#FFFF00", level = 0.99)

  library(compute.es)
 pes(p,n1,n2) #based on p-value
mes(m1,m2,sd1,sd2,n1,n2) #based on means
 tes(t,n1,n2) #based on t-value (t-test)
 fes(F,n1,n2) #based on F (ANOVA)
 res(r,NULL,n) #based on r (e.g. correlation)
 des(d,n1,n2) #based on Cohen's d
 lores(lor,var,n1,n2) #based on Log Oddds Ratio
 pes(p,n1,n2) #based on p-value
d=(2*r)/sqrt(1-(r*r)) #based on r only
r=d/sqrt((d*d)+4) #based on Cohen's d only
d=(2*sqrt(e))/sqrt(1-e) #based on eta2
d=(lor*sqrt(3))/pi #based on Log Odds Ratio only

  library(meta)
 #Calculate Variance ES
es.d.v <-(((n1+n2)/(n1*n2))+(es.d^2/(2*(n1+n2))))
 #Calculate Standard Errors ES
 d.se<-sqrt(es.d.v)
 meta1<-metagen(es.d, d.se)
 forest(meta1, studlab=c("Study1","Study2","Study3","Study4","Study5"), xlab="Cohen’s d", col.square="black",xlim=c(-3,3), col.diamond="black", fontsize=14, squaresize=0.5, leftcols=c("studlab"), rightcols=c("effect", "ci"), hetstat=FALSE, comb.fixed=FALSE, text.random="Overall ES", print.tau2=FALSE,print.I2=FALSE,TE.random=FALSE, seTE.random=FALSE)