Task view: Empirical Finance

Last updated on 2017-09-12 by Dirk Eddelbuettel

This CRAN Task View contains a list of packages useful for empirical work in Finance, grouped by topic.

Besides these packages, a very wide variety of functions suitable for empirical work in Finance is provided by both the basic R system (and its set of recommended core packages), and a number of other packages on the Comprehensive R Archive Network (CRAN). Consequently, several of the other CRAN Task Views may contain suitable packages, in particular the Econometrics , Multivariate, Optimization, Robust, SocialSciences and TimeSeries Task Views.

The ctv package supports these Task Views. Its functions install.views and update.views allow, respectively, installation or update of packages from a given Task View; the option coreOnly can restrict operations to packages labeled as core below.

Contributions are always welcome, and encouraged. Since the start of this CRAN task view in April 2005, most contributions have arrived as email suggestions. The source file for this particular task view file now also reside in a GitHub repository (see below) so that pull requests are also possible.

Standard regression models

  • A detailed overview of the available regression methodologies is provided by the Econometrics task view. This is complemented by the Robust task view, which focuses on more robust and resistant methods.
  • Linear models such as ordinary least squares (OLS) can be estimated by lm() (from by the stats package contained in the basic R distribution). Maximum Likelihood (ML) estimation can be undertaken with the standard optim() function. Many other suitable methods are listed in the Optimization view. Non-linear least squares can be estimated with the nls() function, as well as with nlme() from the nlme package.
  • For the linear model, a variety of regression diagnostic tests are provided by the car, lmtest, strucchange, urca, and sandwich packages. The Rcmdr and Zelig packages provide user interfaces that may be of interest as well.

Time series

  • A detailed overview of tools for time series analysis can be found in the TimeSeries task view. Below a brief overview of the most important methods in finance is given.
  • Classical time series functionality is provided by the arima() and KalmanLike()commands in the basic R distribution.
  • The dse and timsac packages provide a variety of more advanced estimation methods; fracdiff can estimate fractionally integrated series; longmemo covers related material. The fractal provide fractal time series modeling functionality.
  • For volatility modeling, the standard GARCH(1,1) model can be estimated with the garch() function in the tseries package. Rmetrics (see below) contains the fGarch package which has additional models. The rugarch package can be used to model a variety of univariate GARCH models with extensions such as ARFIMA, in-mean, external regressors and various other specifications; with methods for fit, forecast, simulation, inference and plotting are provided too. The rmgarch builds on it to provide the ability to estimate several multivariate GARCH models. The betategarch package can estimate and simulate the Beta-t-EGARCH model by Harvey. The bayesGARCH package can perform Bayesian estimation of a GARCH(1,1) model with Student's t innovations. For multivariate models, the ccgarch package can estimate (multivariate) Conditional Correlation GARCH models whereas the gogarch package provides functions for generalized orthogonal GARCH models. The gets package (which was preceded by a related package AutoSEARCH) provides automated general-to-specific model selection of the mean and log-volatility of a log-ARCH-X model. The GEVStableGarch package can fit ARMA-GARCH or ARMA-APARCH models with GEV and stable conditional distributions. The lgarch package can estimate and fit log-Garch models.
  • Unit root and cointegration tests are provided by tseries, and urca. The Rmetrics packages timeSeries and fMultivar contain a number of estimation functions for ARMA, GARCH, long memory models, unit roots and more. The CADFtest package implements the Hansen unit root test.
  • MSBVAR provides Bayesian estimation of vector autoregressive models. The dlm package provides Bayesian and likelihood analysis of dynamic linear models (ie linear Gaussian state space models).
  • The vars package offer estimation, diagnostics, forecasting and error decomposition of VAR and SVAR model in a classical framework.
  • The dyn and dynlm packages are suitable for dynamic (linear) regression models.
  • Several packages provide wavelet analysis functionality: rwt, wavelets, waveslim, wavethresh. Some methods from chaos theory are provided by the package tseriesChaos. tsDyn adds time series analysis based on dynamical systems theory.
  • The forecast package adds functions for forecasting problems.
  • The tsfa package provides functions for time series factor analysis.
  • The stochvol package implements Bayesian estimation of stochastic volatility using Markov Chain Monte Carlo, and factorstochvol extends this to the multivariate case.
  • The MSGARCH package adds methods to fit (by Maximum Likelihood or Bayesian), simulate, and forecast various Markov-Switching GARCH processes.

Finance

  • The Rmetrics suite of packages comprises fAssets, fBasics, fBonds, timeDate (formerly: fCalendar), fCopulae, fExoticOptions, fExtremes, fGarch, fImport, fNonlinear, fOptions, fPortfolio, fRegression, timeSeries (formerly: fSeries), fTrading, and contains a very large number of relevant functions for different aspect of empirical and computational finance.
  • The RQuantLib package provides several option-pricing functions as well as some fixed-income functionality from the QuantLib project to R. The RcppQuantuccia provides a smaller subset of QuantLib functionality as a header-only library; at current only some calendaring functionality is exposed.
  • The quantmod package offers a number of functions for quantitative modelling in finance as well as data acquisition, plotting and other utilities.
  • The portfolio package contains classes for equity portfolio management; the portfolioSim builds a related simulation framework. The backtest offers tools to explore portfolio-based hypotheses about financial instruments. The pa package offers performance attribution functionality for equity portfolios.
  • The PerformanceAnalytics package contains a large number of functions for portfolio performance calculations and risk management.
  • The TTR contains functions to construct technical trading rules in R.
  • The financial package can compute present values, cash flows and other simple finance calculations.
  • The sde package provides simulation and inference functionality for stochastic differential equations.
  • The termstrc and YieldCurve packages contain methods for the estimation of zero-coupon yield curves and spread curves based the parametric Nelson and Siegel (1987) method with the Svensson (1994) extension. The former package adds the McCulloch (1975) cubic splines approach, the latter package adds the Diebold and Li approach. The SmithWilsonYieldCurve construct the yield curve using the Smith-Wilson approach based on LIBOR and SWAP rates.
  • The vrtest package contains a number of variance ratio tests for the weak-form of the efficient markets hypothesis.
  • The gmm package provides generalized method of moments (GMM) estimations function that are often used when estimating the parameters of the moment conditions implied by an asset pricing model.
  • The tawny package contains estimator based on random matrix theory as well as shrinkage methods to remove sampling noise when estimating sample covariance matrices.
  • The opefimor package by contains material to accompany the Iacus (2011) book entitled "Option Pricing and Estimation of Financial Models in R".
  • The maRketSim package provides a market simulator, initially designed around the bond market.
  • The BurStFin and BurStMisc package has a collection of function for Finance including the estimation of covariance matrices.
  • The AmericanCallOpt package contains a pricer for different American call options.
  • The VarSwapPrice package can price a variance swap via a portfolio of European options contracts.
  • The FinAsym package implements the Lee and Ready (1991) and Easley and O'Hara (1987) tests for, respectively, trade direction, and probability of informed trading.
  • The parma package provides support for portfolio allocation and risk management applications.
  • The GUIDE package provides a GUI for DErivatives and contains numerous pricer examples as well as interactive 2d and 3d plots to study these pricing functions.
  • The SharpeR package contains a collection of tools for analyzing significance of trading strategies, based on the Sharpe ratio and overfit of the same.
  • The RND package implements various functions to extract risk-neutral densities from option prices.
  • The LSMonteCarlo package can price American Options via the Least Squares Monte Carlo method.
  • The BenfordTests package provides seven statistical tests and support functions for determining if numerical data could conform to Benford's law.
  • The OptHedging package values call and put option portfolio and implements an optimal hedging strategy.
  • The markovchain package provides functionality to easily handle and analyse discrete Markov chains.
  • The ycinterextra package models yield curve interpolation and extrapolation using via the Nelson-Siegel, Svensson, or Smith-Wilson models, as well as Hermite cubic splines.
  • The tvm package models provides functions for time value of money such as cashflows and yield curves.
  • The MarkowitzR package provides functions to test the statistical significance of Markowitz portfolios.
  • The pbo package models the probability of backtest overfitting, performance degradation, probability of loss, and the stochastic dominance when analysing trading strategies.
  • The OptionPricing package implements efficient Monte Carlo algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.
  • The matchingMarkets package implements a structural estimator to correct for the bias arising from endogenous matching (e.g. group formation in microfinance or matching of firms and venture capitalists).
  • The restimizeapi package interfaces the API at www.estimize.com which provides crowd-sourced earnings estimates.
  • The credule package is another pricer for credit default swaps.
  • The covmat package provides several different methods for computing covariance matrices.
  • The obAnalytics package analyses and visualizes information from events in limit order book data.
  • The derivmkts package adds a set of pricing and expository functions useful in teaching derivatives markets.
  • The PortfolioEffectHFT package provides portfolio analysis suitable for intra-day and high-frequency data, and also interfaces the PortfolioEffect service.
  • The ragtop package prices equity derivatives under an extension to Black and Scholes supporting default under a power-law link price and hazard rate.
  • The sharpeRratio package adds moment-free estimation of Sharpe ratios.
  • The QuantTools package offers enhanced quantitative trading and modeling tools.
  • The pinbasic package adds tools for fast and stable estimates the Probability of Informed Trading (PIN) by Easley et al, and offers factorizations of the model likelihood. The InfoTrad packages also estimates PIN and extends it different factorization and estimation algorithms.
  • The FinancialMath package contains financial math and derivatives pricing functions as required by the actuarial exams by the Society of Actuaries and Casualty Actuarial Society 'Financial Mathematics' exam.
  • The tidyquant package re-arranges functionality from several other key packages for use in the so-called tidyverse.
  • The BCC1997 prices European options under the Bakshi, Cao anc Chen (1997) model for stochastic volatility, stochastic rates and random jumps.
  • The Sim.DiffProc package provides functions to simulate and analyse multidimensional Itô and Stratonovitch stochastic calculus for continuous-time models.
  • The rpgm package offers fast simulation of normal and exponential random variables and stochastic differential equations.
  • The BLModel package computes the posterior distribution in a Black-Litterman model from a prior distribution given by asset returns and continuous distribution of views given by an external function.
  • The rpatrec package aims to recognise charting patterns in (financial) time series data.
  • The PortfolioOptim can solve both small and large sample portfolio optimization.

Risk management

  • The Task View ExtremeValue regroups a number of relevant packages.
  • The packages CreditMetrics and crp.CSFP provide function for modelling credit risks.
  • The mvtnorm package provides code for multivariate Normal and t-distributions.
  • The Rmetrics packages fPortfolio and fExtremes also contain a number of relevant functions.
  • The copula and fgac packages cover multivariate dependency structures using copula methods.
  • The actuar package provides an actuarial perspective to risk management.
  • The ghyp package provides generalized hyberbolic distribution functions as well as procedures for VaR, CVaR or target-return portfolio optimizations.
  • The ChainLadder package provides functions for modeling insurance claim reserves; and the lifecontingencies package provides functions for financial and actuarial evaluations of life contingencies.
  • The frmqa package aims to collect functions for Financial Risk Management and Quantitative Analysis.
  • The ESG package can be used to model for asset projection, a scenario-based simulation approach.
  • The riskSimul package provides efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolios where log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
  • The GCPM package analyzes the default risk of credit portfolio using both analytical and simulation approaches.
  • The FatTailsR package provides a family of four distributions tailored to distribution with symmetric and asymmetric fat tails.
  • The Dowd package contains functions ported from the 'MMR2' toolbox offered in Kevin Dowd's book "Measuring Market Risk".
  • The PortRisk package computes portfolio risk attribution.
  • The NetworkRiskMeasures package implements some risk measures for financial networks such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.

Books

  • The NMOF package provides functions, examples and data from Numerical Methods and Optimization in Finance by Manfred Gilli, Dietmar Maringer and Enrico Schumann (2011), including the different optimization heuristics such as Differential Evolution, Genetic Algorithms, Particle Swarms, and Threshold Accepting.
  • The FRAPO package provides data sets and code for the book Financial Risk Modelling and Portfolio Optimization with R by Bernhard Pfaff (2013).

Data and date management

  • The zoo and timeDate (part of Rmetrics) packages provide support for irregularly-spaced time series. The xts package extends zoo specifically for financial time series. See the TimeSeries task view for more details.
  • timeDate also addresses calendar issues such as recurring holidays for a large number of financial centers, and provides code for high-frequency data sets.
  • The fame package can access Fame time series databases (but also requires a Fame backend). The tis package provides time indices and time-indexed series compatible with Fame frequencies.
  • The TSdbi package provides a unifying interface for several time series data base backends, and its SQL implementations provide a database table design.
  • The IBrokers package provides access to the Interactive Brokers API for data access (but requires an account to access the service).
  • The data.table package provides very efficient and fast access to in-memory data sets such as asset prices.
  • The TFX package provides an interface to the TrueFX (TM) service for free streaming real-time and historical tick-by-tick market data for interbank foreign exchange rates at the millisecond resolution.
  • The package highfrequency contains functionality to manage, clean and match highfrequency trades and quotes data and enables users to calculate various liquidity measures, estimate and forecast volatility, and investigate microstructure noise and intraday periodicity.
  • The Rbitcoin package offers access to Bitcoin exchange APIs (mtgox, bitstamp, btce, kraken) via public and private API calls and integration of data structures for all markets.
  • The bizdays package compute business days if provided a list of holidays.
  • The TAQMNGR package manages tick-by-tick (equity) transaction data performing 'cleaning', 'aggregation' and 'import' where cleaning and aggregation are performed according to Brownlees and Gallo (2006).
  • The Rblpapi package offers efficient access to the Bloomberg API and allows bdp, bdh, and bds queries as well as data retrieval both in (regular time-)bars and ticks (albeit without subsecond resolution).
  • The finreportr package can download reports from the SEC Edgar database, and relies on, inter alia, the XBRL package for parsing these reports.
  • The GetTDData package imports Brazilian government bonds data (such as LTN, NTN-B and LFT ) from the Tesouro Direto website. The GetHFData package downloads and aggregates tick-by-tick trade data for equity and derivatives markets in Brazil.
  • The fmdates package implements common date calculations according to the ISDA schedules, and can check for business in different locales.

Packages

actuar — 2.1-1

Actuarial Functions and Heavy Tailed Distributions

AmericanCallOpt — 0.95

This package includes pricing function for selected American call options with underlying assets that generate payouts.

backtest — 0.3-4

Exploring Portfolio-Based Conjectures About Financial Instruments

bayesGARCH — 2.1.3

Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations

BCC1997 — 0.1.1

Calculation of Option Prices Based on a Universal Solution

BenfordTests — 1.2.0

Statistical Tests for Evaluating Conformity to Benford's Law

betategarch — 3.3

Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH Models

bizdays — 1.0.4

Business Days Calculations and Utilities

BLModel — 1.0.2

Black-Litterman Posterior Distribution

BurStFin — 1.02

Burns Statistics Financial

BurStMisc — 1.1

Burns Statistics Miscellaneous

CADFtest — 0.3-3

A Package to Perform Covariate Augmented Dickey-Fuller Unit Root Tests

car — 2.1-6

Companion to Applied Regression

ccgarch — 0.2.3

Conditional Correlation GARCH models

ChainLadder — 0.2.5

Statistical Methods and Models for Claims Reserving in General Insurance

copula — 0.999-18

Multivariate Dependence with Copulas

covmat — 1.0

Covariance Matrix Estimation

CreditMetrics — 0.0-2

Functions for calculating the CreditMetrics risk model

credule — 0.1.3

Credit Default Swap Functions

crp.CSFP — 2.0.2

CreditRisk+ Portfolio Model

data.table — 1.10.4-3

Extension of `data.frame`

derivmkts — 0.2.2

Functions and R Code to Accompany Derivatives Markets

dlm — 1.1-4

Bayesian and Likelihood Analysis of Dynamic Linear Models

Dowd — 0.12

Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's Book Measuring Market Risk

dse — 2015.12-1

Dynamic Systems Estimation (Time Series Package)

dyn — 0.2-9.3

Time Series Regression

dynlm — 0.3-5

Dynamic Linear Regression

ESG — 0.1

ESG - A package for asset projection

factorstochvol — 0.8.3

Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

fame — 2.21

Interface for FAME Time Series Database

FatTailsR — 1.7-5

Kiener Distributions and Fat Tails in Finance

fgac — 0.6-1

Generalized Archimedean Copula

financial — 0.2

Solving financial problems in R

fAssets — 3042.84

Rmetrics - Analysing and Modelling Financial Assets

fBasics — 3042.89

Rmetrics - Markets and Basic Statistics

fBonds — 3042.78

Rmetrics - Pricing and Evaluating Bonds

fCopulae — 3042.82

Rmetrics - Bivariate Dependence Structures with Copulae

fExoticOptions — 3042.80

Rmetrics - Pricing and Evaluating Exotic Option

fExtremes — 3042.82

Rmetrics - Modelling Extreme Events in Finance

fGarch — 3042.83

Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

fImport — 3042.85

Rmetrics - Importing Economic and Financial Data

FinancialMath — 0.1.1

Financial Mathematics for Actuaries

FinAsym — 1.0

Classifies implicit trading activity from market quotes and computes the probability of informed trading

finreportr — 1.0.1

Financial Data from U.S. Securities and Exchange Commission

fMultivar — 3042.80

Rmetrics - Analysing and Modeling Multivariate Financial Return Distributions

fNonlinear — 3042.79

Rmetrics - Nonlinear and Chaotic Time Series Modelling

fOptions — 3042.86

Rmetrics - Pricing and Evaluating Basic Options

fPortfolio — 3042.83

Rmetrics - Portfolio Selection and Optimization

fRegression — 3042.82

Rmetrics - Regression Based Decision and Prediction

fTrading — 3042.79

Rmetrics - Trading and Rebalancing Financial Instruments

fmdates — 0.1.3

Financial Market Date Calculations

forecast — 8.2

Forecasting Functions for Time Series and Linear Models

fracdiff — 1.4-2

Fractionally differenced ARIMA aka ARFIMA(p,d,q) models

fractal — 2.0-1

Fractal Time Series Modeling and Analysis

FRAPO — 0.4-1

Financial Risk Modelling and Portfolio Optimisation with R

frmqa — 0.1-5

The Generalized Hyperbolic Distribution, Related Distributions and Their Applications in Finance

GCPM — 1.2.2

Generalized Credit Portfolio Model

GetHFData — 1.4

Download and Aggregate High Frequency Trading Data from Bovespa

gets — 0.13

General-to-Specific (GETS) Modelling and Indicator Saturation Methods

GetTDData — 1.3

Get Data for Brazilian Bonds (Tesouro Direto)

GEVStableGarch — 1.1

ARMA-GARCH/APARCH Models with GEV and Stable Distributions

ghyp — 1.5.7

A Package on Generalized Hyperbolic Distribution and Its Special Cases

gmm — 1.6-1

Generalized Method of Moments and Generalized Empirical Likelihood

gogarch — 0.7-2

Generalized Orthogonal GARCH (GO-GARCH) models

GUIDE — 1.2.3.1

GUI for DErivatives in R

highfrequency — 0.5.2

Tools for Highfrequency Data Analysis

IBrokers — 0.9-12

R API to Interactive Brokers Trader Workstation

InfoTrad — 1.2

Calculates the Probability of Informed Trading (PIN)

lgarch — 0.6-2

Simulation and Estimation of Log-GARCH Models

lifecontingencies — 1.3.1

Financial and Actuarial Mathematics for Life Contingencies

lmtest — 0.9-35

Testing Linear Regression Models

LSMonteCarlo — 1.0

American options pricing with Least Squares Monte Carlo method

longmemo — 1.0-0

Statistics for Long-Memory Processes (Jan Beran) -- Data and Functions

markovchain — 0.6.9.8-1

Easy Handling Discrete Time Markov Chains

maRketSim — 0.9.2

Market simulator for R

MarkowitzR — 0.9900.0

Statistical Significance of the Markowitz Portfolio

matchingMarkets — 0.3-3

Analysis of Stable Matchings

MSBVAR — 0.9-3

Markov-Switching, Bayesian, Vector Autoregression Models

MSGARCH — 2.0

Markov-Switching GARCH Models

mvtnorm — 1.0-6

Multivariate Normal and t Distributions

NetworkRiskMeasures — 0.1.2

Risk Measures for (Financial) Networks

nlme — 3.1-131

Linear and Nonlinear Mixed Effects Models

NMOF — 1.2-2

Numerical Methods and Optimization in Finance

obAnalytics — 0.1.1

Limit Order Book Analytics

opefimor — 1.2

Option Pricing and Estimation of Financial Models in R

OptHedging — 1.0

Estimation of value and hedging strategy of call and put options.

OptionPricing — 0.1

Option Pricing with Efficient Simulation Algorithms

pa — 1.2-1

Performance Attribution for Equity Portfolios

parma — 1.5-3

Portfolio Allocation and Risk Management Applications

pbo — 1.3.4

Probability of Backtest Overfitting

PerformanceAnalytics — 1.4.3541

Econometric tools for performance and risk analysis

pinbasic — 1.1.0

Fast and Stable Estimation of the Probability of Informed Trading (PIN)

portfolio — 0.4-7

Analysing equity portfolios

PortfolioEffectHFT — 1.8

High Frequency Portfolio Analytics by PortfolioEffect

PortfolioOptim — 1.0.3

Small/Large Sample Portfolio Optimization

portfolioSim — 0.2-7

Framework for simulating equity portfolio strategies

PortRisk — 1.1.0

Portfolio Risk Analysis

quantmod — 0.4-11

Quantitative Financial Modelling Framework

QuantTools — 0.5.6

Enhanced Quantitative Trading Modelling

ragtop — 0.5

Pricing Equity Derivatives with Extensions of Black-Scholes

Rbitcoin — 0.9.2

R & bitcoin integration

Rblpapi — 0.3.6

R Interface to 'Bloomberg'

Rcmdr — 2.4-1

R Commander

RcppQuantuccia — 0.0.2

R Bindings to the 'Quantuccia' Header-Only Essentials of 'QuantLib'

restimizeapi — 1.0.0

Functions for Working with the 'www.estimize.com' Web Services

riskSimul — 0.1

Risk Quantification for Stock Portfolios under the T-Copula Model

rmgarch — 1.3-0

Multivariate GARCH Models

RND — 1.2

Risk Neutral Density Extraction Package

rpatrec — 1.0.1

Recognising Visual Charting Patterns in Time Series Data

rpgm — 1.1.1

Fast Simulation of Normal/Exponential Random Variables and Stochastic Differential Equations / Poisson Processes

RQuantLib — 0.4.4

R Interface to the 'QuantLib' Library

rugarch — 1.3-8

Univariate GARCH Models

rwt — 1.0.0

Rice Wavelet Toolbox wrapper

sandwich — 2.4-0

Robust Covariance Matrix Estimators

sde — 2.0.15

Simulation and Inference for Stochastic Differential Equations

SharpeR — 1.1.0

Statistical Significance of the Sharpe Ratio

sharpeRratio — 1.1

Moment-Free Estimation of Sharpe Ratios

Sim.DiffProc — 3.8

Simulation of Diffusion Processes

SmithWilsonYieldCurve — 1.0.1

Smith-Wilson Yield Curve Construction

stochvol — 1.3.3

Efficient Bayesian Inference for Stochastic Volatility (SV) Models

strucchange — 1.5-1

Testing, Monitoring, and Dating Structural Changes

TAQMNGR — 2016.12-1

Manage Tick-by-Tick Transaction Data

timsac — 1.3.5

Time Series Analysis and Control Package

tseries — 0.10-42

Time Series Analysis and Computational Finance

termstrc — 1.3.7

Zero-coupon Yield Curve Estimation

TFX — 0.1.0

R API to TrueFX(tm)

TSdbi — 2017.4-1

Time Series Database Interface

tsDyn — 0.9-44

Nonlinear Time Series Models with Regime Switching

tseriesChaos — 0.1-13

Analysis of nonlinear time series

tsfa — 2014.10-1

Time Series Factor Analysis

TTR — 0.23-2

Technical Trading Rules

tawny — 2.1.6

Clean Covariance Matrices Using Random Matrix Theory and Shrinkage Estimators for Portfolio Optimization

tidyquant — 0.5.3

Tidy Quantitative Financial Analysis

timeDate — 3042.101

Rmetrics - Chronological and Calendar Objects

timeSeries — 3042.102

Rmetrics - Financial Time Series Objects

tis — 1.32

Time Indexes and Time Indexed Series

tvm — 0.3.0

Time Value of Money Functions

urca — 1.3-0

Unit Root and Cointegration Tests for Time Series Data

vars — 1.5-2

VAR Modelling

vrtest — 0.97

Variance Ratio tests and other tests for Martingale Difference Hypothesis

VarSwapPrice — 1.0

Pricing a variance swap on an equity index

wavelets — 0.3-0

A package of functions for computing wavelet filters, wavelet transforms and multiresolution analyses

waveslim — 1.7.5

Basic wavelet routines for one-, two- and three-dimensional signal processing

wavethresh — 4.6.8

Wavelets Statistics and Transforms

ycinterextra — 0.1

Yield curve or zero-coupon prices interpolation and extrapolation

YieldCurve — 4.1

Modelling and estimation of the yield curve

XBRL — 0.99.18

Extraction of Business Financial Information from 'XBRL' Documents

xts — 0.10-0

eXtensible Time Series

Zelig — 5.1.5

Everyone's Statistical Software

zoo — 1.8-0

S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)


Task view list