Lag/Lead Correlation

Description

Calculating correlation of two vectors with lag and lead periods. The correlations are used to determine the lag or lead effect between two variables. The correlation function uses "na.or.complete" method and calculate the Pearson's correlation.

Usage

cor.lag(x,y,lag,lead)

Arguments

Examples

cor.lag(mtcars[,1],mtcars[,2],3,3)

Spearman rank correlation

Description

Calculate Spearman Rank Correlation, which is the nonparametric version of the Pearson product-moment correlation.

Usage

cor.spearman(x,y)

Arguments

Examples

 cor.spearman(mtcars[,1], mtcars[,3])

Calculate future value of annuity

Description

Calculate future value of an ordinary annuity or an annuity due.

Usage

cv.annu.fv(pmt,i,n,type = 0)

Arguments

Examples

cv.annu.fv(100,0.0248,10,0)

Calculate present value of annuity

Description

Calculate present value of an ordinary annuity or an annuity due.

Usage

cv.annu.pv(pmt,i,n,k)

Arguments

Examples

cv.annu.pv(100,0.0248,10,4)

Create logarithm with a random base

Description

Create a new variable with the base of a random number and power of the selected variable

Usage

cv.axp(dataframe, var, n, range)

Arguments

Examples

cv.axp(mtcars,"wt",5,c(1, 2))

Calculate the plain vanilla bond price

Description

Calculate the plain vanilla bond price

Usage

cv.bondprice(par,c,yield,n,m)

Arguments

Examples

cv.bondprice(1000,0.0248,0.0248,10,2)

Calculating the difference of a time series

Description

Calculate the difference of a time series, with a specific lag period. The difference is used to show the change in value over set period.

Usage

cv.diff(x,n)

Arguments

Examples

cv.diff(mtcars[,2],1)

Largest draw down of returns

Description

Calculate largest draw down of a series of returns. This function calculates the maximum decrease in percentage over time, which can be used to test portfolio returns.

Usage

cv.drawdown(x)

Arguments

Examples

# rnorm() is used to simulate portfolio returns
returns <- rnorm(100)
cv.drawdown(returns)

Create a lag variable

Description

Create a lag variable, with a choice of lag periods. The lag variable can be used to test lag effects between variables.

Usage

cv.lag(x,n)

Arguments

Examples

cv.lag(mtcars[,2],3)
data.frame(mtcars,cv.lag(mtcars[,3], 1))

Create a lead variable

Description

Create a lead variable, with a choice of lead periods. The lead variable can be used to test lead effects between variables.

Usage

cv.lead(x,n)

Arguments

Examples

cv.lead(mtcars[,2],3)
data.frame(mtcars,cv.lead(mtcars[,3], 3))

Create logarithm with a random base

Description

Create a new variable that is the logarithm of the selected variable with the base of a random number

Usage

cv.logs(dataframe, var, n, range)

Arguments

Examples

cv.logs(mtcars,"wt",5,c(1, 2))

Calculating rate of return of a vector

Description

Calculating the percentage change of a time series vector for further analysis, including calculating beta of companies, plotting to see the trend of the stock for technical analysis.

Usage

cv.pctcng(x,n)

Arguments

Examples

cv.pctcng(mtcars[,1],1)

Create nth power variable

Description

Create a new variable that is the nth power of the selected variable

Usage

cv.powers(dataframe, var, n, range)

Arguments

Examples

cv.powers(mtcars,"wt",5,c(1, 2))

Sort a data frame by a column

Description

Sort a data frame by a column of choice. The column of choice is specified by the number of the column.

Usage

df.sortcol(x,n,desc)

Arguments

Examples

df.sortcol(mtcars,2,desc = TRUE)

Stack data frame by one classifier

Description

Stack data frame by one classifier. This function takes the first column as a ordering variable. Then it take the variables names and repeat as the second column. The last column will be data under each variable name. This function is created to enable easier investigation with apply functions.

Usage

df.stack(df,name)

Arguments

Examples

df <- data.frame(matrix(nrow=100,ncol=100))
for(i in 1:100){
 df[,i] <- rep(runif(1,1,100),100)
}
dim(df)
hdf <- df.stack(df,c("date","tkr","price"))

Correlation matrix

Description

Calculating the correlation matrix of a data frame and return in a data frame object

Usage

ds.corm(x,n)

Arguments

Examples

ds.corm(mtcars,3)

Calculating kurtosis for numeric data.

Description

Kurtosis

Usage

ds.kurt(x)

Arguments

Examples

ds.kurt(mtcars[,2])

Calculating mode for numeric data

Description

Calculating mode for numeric data.

Usage

ds.mode(x)

Arguments

Examples

ds.mode(mtcars[,2])

Calculating skewness for numeric data

Description

Calculating Pearson's skewness in three types: mode, median, and mean.

Usage

ds.skew(x, type = 3)

Arguments

Examples

ds.skew(mtcars[,1])

Descriptive statistics of a data frame

Description

Calculating the descriptive statistics of a data frame and exporting in a data frame. The report data frame contains: number of observations, maximum value, minimum value, mean, median, mode, variance, standard deviation, skewness and kurtosis.

Usage

ds.summ(x,n)

Arguments

Examples

ds.summ(mtcars,3)

Time series plot for two variables

Description

Plotting two time series in one plot, with title.

Usage

pl.2ts(ts1,ts2,title)

Arguments

Examples

DAX <- EuStockMarkets[,1]
FTSE <- EuStockMarkets[,4]
pl.2ts(DAX,FTSE, "Times Series Plot of DAX and FTSE")

Time series plot for two variables with ggplot2

Description

Plotting two time series in one plot, with title and label. If both variables are time series object, they will be merged by time. If both variables are not time series object, they will be merged by order. The first variable is set to be a solid line and the second variable is set to be a dashed line. If the variables are of different type a warning message will be given.

Usage

pl.2tsgg(ts1,ts2,title,ylab)

Arguments

Examples

DAX <- EuStockMarkets[,1]
FTSE <- EuStockMarkets[,4]
pl.2tsgg(DAX,FTSE, "Times Series Plot of DAX and FTSE", "Index")

Scatter smooth plot with text overlay

Description

Generate a scatter plot with text overlay, with a smooth curve fitted by loess.

Usage

pl.3smoothtxt(x,y,txt,ce)

Arguments

Examples

pl.3smoothtxt(mtcars[,1], mtcars[,3], row.names(mtcars))

Scatter smooth plot with text overlay using ggplot2

Description

Generate a scatter plot with text overlay, with a smooth curve fitted by loess.

Usage

pl.3smoothtxtgg(x,y,txt,size,title,xlab,ylab)

Arguments

Examples

pl.3smoothtxtgg(mtcars[,1], mtcars[,3], row.names(mtcars), 3, "MPG v. DISP","mpg","disp")

Scatter plot with text overlay

Description

Generate a scatter plot with text overlay. This plot is to better show the effect of the text variable in the domain of x and y variable.

Usage

pl.3txt(x,y,txt,title)

Arguments

Examples

pl.3txt(mtcars[,1], mtcars[,3], row.names(mtcars),"mpg v. cyl")

Scatter plot with text overlay with ggplot2

Description

Generate a scatter plot with text overlay with ggplot2. This plot is to better show the effect of the text variable in the domain of x and y variable.

Usage

pl.3txtgg(x,y,txt,size,title,xlab,ylab)

Arguments

Examples

pl.3txtgg(mtcars[,1], mtcars[,3], row.names(mtcars), 3,"mpg v. cyl", "mpg", "cyl")

Scatter plot of x and y divided by z

Description

Generate 4 scatter plots of x and y divided by variable z, with a fitted line using a simple linear regression method.

Usage

pl.coplot(x,y,z,varN)

Arguments

Examples

pl.coplot(mtcars[,1], mtcars[,3], mtcars[,4], "hp")

Plot histograms for a data frame

Description

Plotting histograms for a data frame, with titles and label numbers.

Usage

pl.hist(x, l = 1)

Arguments

Examples

pl.hist(mtcars,1)

Plot histograms for a data frame with ggplot2

Description

Plotting histograms for a data frame with 4 per page, with titles and label numbers automatically generated.

Usage

pl.histgg(x,l,bin)

Arguments

Examples

pl.histgg(as.data.frame(EuStockMarkets),1)

Plot histograms and scatter plots for a data frame

Description

Plotting histograms or scatter plots of your choice for a data frame. Also the function will name the graphs and number them. The purpose of the function is to save time when plotting graphs for a regression analysis or other usage. The function can plot, name and number the graphs at one step.

Usage

pl.hs(x,a,dependent,l)

Arguments

Examples

pl.hs(mtcars,0,"mpg",1)

Plot histogram with density line for a data frame

Description

Plotting histogram with density for a data frame, with titles and label numbers.

Usage

pl.hsd(dataframe,l)

Arguments

Examples

pl.hsd(mtcars,1)

Plot histograms for a data frame with ggplot2

Description

Plotting histograms for a data frame with 4 per page, with titles and label numbers automatically generated.

Usage

pl.hsdgg(x,l,bin)

Arguments

Examples

pl.hsdgg(as.data.frame(EuStockMarkets),1,100)

Plot mean-variance simulation result

Description

This function is used to plot the result of portfolio simulation by pt.mv().

Usage

pl.mv(port)

Arguments

Examples

set.seed(1)
rtn <- data.frame(runif(120,-1,1),runif(120,-1,1),runif(120,-1,1),runif(120,-1,1))
names(rtn) <- c("asset1","asset2","asset3","asset4")
portfolio <- pt.hismv(rtn,1000,0)
pl.mv(portfolio)

Plot scatter plots for a data frame

Description

Plotting scatter plots for a data frame, with titles and label numbers.

Usage

pl.s(x,dependent,l)

Arguments

Examples

pl.s(mtcars,"mpg",1)

Plot scatter plots for a data frame using ggplot2

Description

Plotting scatter plots for a data frame using ggplot2, with titles and label numbers. The output will be 4 graphs per page.

Usage

pl.sgg(x,dependent,l)

Arguments

Examples

pl.sgg(mtcars,"mpg",1)

Plot scatter smooth plots for a data frame

Description

Plotting scatter smooth plots for a data frame, with titles and label numbers.

Usage

pl.sm(x,dependent,l)

Arguments

Examples

pl.sm(mtcars,"mpg",1)

Plot scatter plots with smooth line for a data frame using ggplot2

Description

Plotting scatter plots for a data frame using ggplot2, with titles and label numbers. A smooth line will be added using a chosen method. The output will be 4 graphs per page.

Usage

pl.smgg(x,dependent,l,mtd)

Arguments

Examples

pl.smgg(mtcars,"mpg",1,lm)
pl.smgg(mtcars,"mpg",1,loess)

Plot time series plots for a data frame

Description

Plotting time series plots for a data frame, with titles and label numbers.

Usage

pl.ts(x, l = 1)

Arguments

Examples

pl.ts(mtcars,1)

Plot times series plot for a data frame with ggplot2

Description

Plotting time series plot for a data frame with 4 per page, with titles and label numbers automatically generated.

Usage

pl.tsgg(x,l)

Arguments

Examples

pl.tsgg(as.data.frame(EuStockMarkets),1)

Time series plot with multiple variables

Description

This function will return a time series plot with up to 6 variables, each with different line type.

Usage

pl.tss(dataframe,ylb,title)

Arguments

Examples

pl.tss(EuStockMarkets,"Price","Daily Closing Prices of Major European Stock Indices")

Stock return alpha

Description

Alpha is the intercept of a fitted line when dependent variable is the benchmark return and independent variable is a asset return of the same period. It is a measure of the active return on an investment. Alpha, along with beta, is one of the two key coefficients in the CAPM used modern portfolio theory.

Usage

pt.alpha(ar,br)

Arguments

Examples

brtn <- runif(100, -1, 1)
artn <- runif(100, -1, 1)
pt.alpha(artn,brtn)

Annualized excess return

Description

Annualized excess return is the difference between the annualized and cumulative return of the two series. Usually, one series are portfolio returns and the other is a benchmark returns.

Usage

pt.annexrtn(ar,br)

Arguments

Examples

artn <- runif(100, -1, 1)
brtn <- runif(100, -1, 1)
pt.annexrtn(artn, brtn)

Annualized return

Description

This function takes a series of annual returns and calculate the annualized return.

Usage

pt.annrtn(r,n)

Arguments

Examples

r <- runif(100,-1,1) # generate random number to simulate returns
annualizedreturn <- pt.annrtn(r,100)

Annualized standard deviation

Description

The annualized standard deviation is the standard deviation multiplied by the square root of the number of periods in one year.

Usage

pt.annsd(r,n)

Arguments

Examples

rtn <- runif(30, -1, 1)
n <- 30
pt.annsd(rtn,n)

Stock return beta

Description

Beta is the slope of a fitted line when dependent variable is the benchmark return and independent variable is an asset return of the same period. It is a measure the risk arising from exposure to general market movements.

Usage

pt.beta(ar,br)

Arguments

Examples

brtn <- runif(100, -1, 1)
artn <- runif(100, -1, 1)
pt.beta(artn, brtn)

Bias ratio

Description

The bias ratio is an indicator used in finance analyze the returns of a portfolio, and in performing due diligence.

Usage

pt.bias(r)

Arguments

Examples

r <- runif(100,0,1) # generate random number to simulate returns
pt.bias(r)

Batting average

Description

The batting average of the asset is the ratio between the number of periods where the asset outperforms a benchmark and the total number of periods.

Usage

pt.btavg(ar,br)

Arguments

Examples

artn <- runif(100,-1,1)
brtn <- runif(100,-1,1)
pt.btavg(artn,brtn)

Cumulative excess return

Description

Cumulative return is the compounded return in a given period. The excess return is the difference between the cumulative return of a risky asset and the cumulative return of a benchmark.

Usage

pt.cmexrtn(ar,br)

Arguments

Examples

brtn <- runif(12, -1, 1)
artn <- runif(12, -1, 1)
pt.cmexrtn(artn,brtn)

Cumulative return

Description

Cumulative return is the compounded return in a given period.

Usage

pt.cmrtn(r)

Arguments

Examples

rt <- runif(12,-1,1) # generate random number to simulate returns
pt.cmrtn(rt)

Dual-alpha

Description

Dual-alpha method is to divide market alpha into downside beta and upside alpha. The principle behind is that upside and downside alphas are not the same.

Usage

pt.dalpha(ar,mr,rf)

Arguments

Examples

artn <- runif(24,0,1) # generate random number to simulate returns
mrtn <- runif(24,-1,1)
pt.dalpha(artn,mrtn,0.024)

Dual-beta

Description

Dual-beta method is to divide market beta into downside beta and upside beta. The principle behind is that upside and downside betas are not the same.

Usage

pt.dbeta(ar,mr,rf)

Arguments

Examples

artn <- runif(24,0,1) # generate random number to simulate returns
mrtn <- runif(24,-1,1)
pt.dbeta(artn,mrtn,0.024)

Expected loss

Description

This function give the expected loss of given asset returns.

Usage

pt.exploss(r,p)

Arguments

Examples

rt <- runif(12,-1,1) # generate random number to simulate returns
pt.exploss(rt,0)
pt.exploss(rt,1)

Mean-variance model with historical average returns and standard deviations

Description

This function will perform portfolio simulation with historical average returns and standard deviatoins. Mean-variance model, or modern portfolio theory, is a mathmatical framework for accessing a portfolio. It uses the variance of asset returns as a risk proxy. This function will return a number of simulated portfolio with different weights.

Usage

pt.hismv(r,n,mini)

Arguments

Examples

set.seed(20)
rtn <- data.frame(runif(120,-1,1),runif(120,-1,1),runif(120,-1,1),runif(120,-1,1))
names(rtn) <- c("asset1","asset2","asset3","asset4")
portfolio <- pt.hismv(rtn,1000,0)
plot(portfolio[,6], portfolio[,5], xlab = "standart deviation", ylab = "expected return")

Information ratio

Description

The information ratio of asset's returns versus benchmark returns, is the quotient of the annualized excess return and the annualized standard deviation of the excess return.

Usage

pt.info(ar,br,n)

Arguments

Examples

brtn <- runif(100, -1, 1)
artn <- runif(100, 0, 1)
pt.info(artn,brtn,100)

Jensen's alpha

Description

Jensen's alpha is a financial statistic used to quantify the abnormal return of a security or portfolio over the theoretical expected return. Unlike, standard alpha, it uses theoretical performance return instead of a market return.

Usage

pt.jalpha(pr,mr,rf,beta)

Arguments

Examples

prtn <- runif(24, -1, 1)
mrtn <- runif(24, -1, 1)
rf <- 0.024
pt.jalpha(mean(prtn), mean(mrtn), rf, pt.beta(prtn,mrtn))

Modigliani risk-adjusted performance

Description

Modigliani risk-adjusted performance is a financial measure of risk-adjusted returns of a portfolio. It measures the returns of the portfolio after adjusting it relative to some benchmark.

Usage

pt.m2(pr,br,rf)

Arguments

Examples

prtn <- runif(12,-1,1)
brtn <- runif(12,-1,1)
rf <- 0.024
pt.m2(prtn,brtn,rf)

Probability of loss

Description

This function give the probability of loss of given asset returns.

Usage

pt.probloss(r,p)

Arguments

Examples

rt <- runif(12,-1,1) # generate random number to simulate returns
pt.probloss(rt,0)
pt.probloss(rt,0.05)

Roy's safety-first criterion

Description

Roy's safety-first criterion is a risk management technique that allows to choose a portfolio based on the criterion that the probability of the portfolio's return falling below a minimum desired threshold is minimized.

Usage

pt.roy(r,mar)

Arguments

Examples

r <- runif(100,0,1) # generate random number to simulate returns
pt.roy(r,0.024)

Standard deviation of excess return

Description

The standard deviation of excess return is simply the standard deviation of the asset return over the benchmark return.

Usage

pt.sdexrtn(ar,br)

Arguments

Examples

artn <- runif(12, -1, 1)
brtn <- runif(12,-1,1)
pt.sdexrtn(artn,brtn)

Semivariance of loss

Description

This function give the semivariance of a losing scenario.

Usage

pt.semivar(r,p)

Arguments

Examples

rt <- runif(12,-1,1) # generate random number to simulate returns
pt.semivar(rt,0)
pt.semivar(rt,0.03)

Sharp ratio

Description

The Sharpe Ratio of an asset return is the quotient of the annualized excess return of the asset minus the annualized risk-free rate over the annualized standard deviation of the asset return.

Usage

pt.sharp(r,n,m,rf)

Arguments

Examples

set.seed(20)
rtn <- runif(12,-0.5,1) # generate random number to simulate monthly returns
rfr <- 0.024 # set risk free rate at 2.4% annual
pt.sharp(rtn,1,12,rfr) # the return is for one year

Sortino ratio

Description

The Sortino ratio is an analog to the sharp ratio, with standard deviation replaced by the downside deviation.

Usage

pt.sortino(r,p,n,rf)

Arguments

Examples

rtn <- runif(12, -1, 1)
pt.sortino(rtn,0.3,1,0.024)

Tracking error

Description

Tracking error, in finance, is a measure of risk in a portfolio that is due to active management decisions made by the manager. It indicates how closely the portfolio follows the benchmark of choosing.

Usage

pt.te(pr,br)

Arguments

Examples

prtn <- runif(12,-1,1)
brtn <- runif(12,-1,1)
pt.te(prtn,brtn)

Treynor ratio

Description

The Treynor ratio is an analog to the sharp ratio, with standard deviation replaced by the asset beta to benchmark.

Usage

pt.treynor(ar,br,n,rf)

Arguments

Examples

rtn <- runif(24, -1, 1)
brtn <- runif(24,-1,1)
pt.treynor(rtn,brtn,2,0.024)

Average up and down returns

Description

This function calculates the average up and down returns from a series of returns.

Usage

pt.udrtn(r)

Arguments

Examples

r <- runif(100,-1,1) # generate random number to simulate returns
pt.udrtn(r)

Up and down capture

Description

The up and down capture is a measure of how an asset was able to improve on benchmark returns or how it underperforms over the benchmark.

Usage

pt.updwcap(ar,br,n)

Arguments

Examples

artn <- runif(12, -1, 1)
brtn <- runif(12,-1,1)
pt.updwcap(artn,brtn,1)

Adjusted R-squared for lm.fit

Description

Calculate Adjusted R-squared for the outcome of lm.fit. This function is built for reg.linreg() for higher efficiency only. It can't be used for calculating Adjusted R-squared in general operation.

Usage

reg.adj.r.squared(r,n,p)

Arguments

Examples

X <- as.matrix(cbind(1,EuStockMarkets[,1:2])) # create the design matrix
Y <- as.data.frame(EuStockMarkets)$FTSE
fit <- lm.fit(x = X, y = Y)
SSR <- sum((fit$fitted.values - mean(Y))^2)
SSTO <- sum((Y - mean(Y))^2)
r <- reg.r.squared(SSR,SSTO)
n <- dim(X)[1]; p <- dim(X)[2]
reg.adj.r.squared(r,n,p)

AIC for lm.fit

Description

Calculate AIC for the outcome of AIC. This function is built for reg.linreg for higher efficiency only. It can't be used for calculating AIC in general operation.

Usage

reg.aic(fit,w)

Arguments

Examples

X <- as.matrix(cbind(1,EuStockMarkets[,1:2])) # create the design matrix
Y <- as.data.frame(EuStockMarkets)$FTSE
fit <- lm.fit(x = X, y = Y)
w <- rep(1,length(Y))
reg.aic(fit,w)

BIC for lm.fit

Description

Calculate BIC for the outcome of lm.fit This function is built for reg.linreg() for higher efficiency only. It can't be used for calculating BIC in general operation.

Usage

reg.bic(fit,w)

Arguments

Examples

X <- as.matrix(cbind(1,EuStockMarkets[,1:2])) # create the design matrix
Y <- as.data.frame(EuStockMarkets)$FTSE
fit <- lm.fit(x = X, y = Y)
w <- rep(1,length(Y))
reg.bic(fit,w)

Degree of freedom for lim.fit

Description

Calculate degree of freedom for the outcome of lm.fit(). This function is built for reg.linreg for higher efficiency only. It can't be used for calculating degree of freedom in general operation.

Usage

reg.dof(fit)

Arguments

Examples

X <- as.matrix(cbind(1,EuStockMarkets[,1:2])) # create the design matrix
Y <- as.data.frame(EuStockMarkets)$FTSE
fit <- lm.fit(x = X, y = Y)
reg.dof(fit)

Durbin-Watson Test

Description

Performs the Durbin-Watson Test for a regression model

Usage

reg.dw(fit)

Arguments

Examples

fit <- lm(mpg~wt, mtcars, na.action = na.omit)
reg.dw(fit)

Linear regression processor

Description

This function will take a data frame and the dependent variable and fit all possible combinations of models. The result will be a data frame of models and test statistics for all the models possible. The test statistics are current set and contain all the following: R-squared, Adjusted R-squared, Degree of freedom, Residual standard error, AIC, BIC, Durbin-Watson statistic.

Usage

reg.linreg(dataframe,dependent)

Arguments

Examples

reg.linreg(mtcars,"mpg")

Linear model generator

Description

This function will take a data frame and generate all the combinations of linear model

Usage

reg.model(dataframe,dependent)

Arguments

Examples

reg.model(mtcars,"mpg")

R-squared for lm.fit

Description

Calculate R-squared for the outcome of lm.fit(). This function is built for reg.linreg for higher efficiency only. It can't be used for calculating R-squared in general operation.

Usage

reg.r.squared(SSR,SSTO)

Arguments

Examples

X <- as.matrix(cbind(1,EuStockMarkets[,1:2])) # create the design matrix
Y <- as.data.frame(EuStockMarkets)$FTSE
fit <- lm.fit(x = X, y = Y)
me <- mean(Y)
SSR <- sum((fit$fitted.values - me)^2)
SSTO <- sum((Y - me)^2)
reg.r.squared(SSR,SSTO)

Standard error for lim.fit

Description

Calculate standard error for the outcome of lm.fit(). This function is built for reg.linreg for higher efficiency only. It can't be used for calculating standard error in general operation.

Usage

reg.std.err(SSE,dof)

Arguments

Examples

X <- as.matrix(cbind(1,EuStockMarkets[,1:2])) # create the design matrix
Y <- as.data.frame(EuStockMarkets)$FTSE
fit <- lm.fit(x = X, y = Y)
SSE <- sum((Y - fit$fitted.values)^2)
dof <- reg.dof(fit)
reg.std.err(SSE,dof)

Sigmoid function

Description

Generate sigmoid curve series, which is a specific case of logistic function, with a control of top and bottom acceleration.

Usage

tr.log(x,top,a,b)

Arguments

Examples

 sigc <- round(tr.log(seq(-3, 3, 0.1), 1, -3, 3), 3)
ts.plot(sigc)

Logistic function

Description

Generate logistic series, with set top and bottom value and acceleration.

Usage

tr.logtb(x,top,bot,a,b)

Arguments

Examples

tr.logtb(seq(-3, 3, 0.1), 1, 0.4, -3, 3)

Normal density function

Description

Calculate normal density function value at x with a mean of mu and standard deviation of sig.

Usage

tr.nd(x,mu,sig)

Arguments

Examples

 tr.nd(seq(-3, 3, 0.1), 0, 1)

Unit normal loss integral

Description

Compute the value of the unit normal loss integral, with discontinuity and dispersion

Usage

tr.unli(x,disc,disp)

Arguments

Examples

 tr.unli(-3:10, 1, 3)

Download data from Federal Reserve Bank of St. Louis

Description

This function returns a data from the Federal Reserve Bank of St. Louis database. It can take one ticker or a string of tickers, which will output a merged data frame with all observations.

Usage

xd.fred(tkr, start_date, end_date)

Arguments

Examples

 cpi <- xd.fred("CPIAUCSL") # CPI data
head(cpi)
tail(cpi)

#Frequently used tickers:
#CPIAUCSL: Consumer Price Index for All Urban Consumers: All Items
#A191RL1Q225SBEA: Real Gross Domestic Product
#DGS10: 10-Year Treasury Constant Maturity Rate
#UNRATE: Civilian Unemployment Rate

Federal Reserve Bank of St. Louis Economic Data Tickers

Description

This function returns a data contains information of data name, type and tickers

Usage

xd.fred.tickers()

Examples

xd.fred.tickers()