Materias: “…R (Computer program language)…”

Materias:

Tabla de Contenidos: “…Limitation of the individual classifiers -- Ensemble classifiers methods -- Classification with individual and ensemble trees in R -- Bankrupcty prediction through ensemble trees -- Experiments with adabag in biology classification tasks -- Generalization bounds for ranking algorithms -- Classification and regression trees for analysing irrigation decisions -- Boosted rule learner and its properties -- Credit scoring with individuals and ensemble trees -- An overview of multiple classifier systems based on GAM…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Setting Up Data Analysis Environment -- Chapter 1: Setting Up Our Data Analysis Environment -- Technical requirements -- The benefits of EDA across vertical markets -- Manipulating data -- Examining, cleaning, and filtering data -- Visualizing data -- Creating data reports -- Installing the required R packages and tools -- Installing R packages from the Terminal -- Installing R packages from inside RStudio -- Summary -- Chapter 2: Importing Diverse Datasets -- Technical requirements -- Converting rectangular data into R with the readr R package -- readr read functions -- read_tsv method -- read_delim method -- read_fwf method -- read_table method -- read_log method -- Reading in Excel data with the readxl R package -- Reading in JSON data with the jsonlite R package -- Loading the jsonlite package -- Getting data into R from web APIs using the httr R package -- Getting data into R by scraping the web using the rvest package -- Importing data into R from relational databases using the DBI R package -- Summary -- Chapter 3: Examining, Cleaning, and Filtering -- Technical requirements -- About the dataset -- Reshaping and tidying up erroneous data -- The gather() function -- The unite() function -- The separate() function -- The spread() function -- Manipulating and mutating data -- The mutate() function -- The group_by() function -- The summarize() function -- The arrange() function -- The glimpse() function -- Selecting and filtering data -- The select() function -- The filter() function -- Cleaning and manipulating time series data -- Summary -- Chapter 4: Visualizing Data Graphically with ggplot2 -- Technical requirements -- Advanced graphics grammar of ggplot2 -- Data -- Layers -- Scales…”

Materias:

Tabla de Contenidos: “…Contents at a Glance; Contents; About the Authors; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Introduction to the Real-Time Web and ASP.NET SignalR; Evolution of the Internet; Why the Client-Side Experience Is More Important than Ever; Real-Time Web Application Development; Examples of Real-Time Web Application Development; Facebook; Twitter; Google Search; Google Docs; JabbR; ShootR; History of ASP.NET SignalR; What Is ASP.NET SignalR?…”

Materias:

Materias:

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Preface -- 1: Financial Data and Their Properties -- 1.1 Asset Returns -- 1.2 Bond Yields and Prices -- 1.3 Implied Volatility -- 1.4 R Packages and Demonstrations -- 1.4.1 Installation of R Packages -- 1.4.2 The Quantmod Package -- 1.4.3 Some Basic R Commands -- 1.5 Examples of Financial Data -- 1.6 Distributional Properties of Returns -- 1.6.1 Review of Statistical Distributions and Their Moments -- 1.7 Visualization of Financial Data -- 1.8 Some Statistical Distributions -- 1.8.1 Normal Distribution -- 1.8.2 Lognormal Distribution -- 1.8.3 Stable Distribution -- 1.8.4 Scale Mixture of Normal Distributions -- 1.8.5 Multivariate Returns -- Exercises -- References -- 2: Linear Models for Financial Time Series -- 2.1 Stationarity -- 2.2 Correlation and Autocorrelation Function -- 2.3 White Noise and Linear Time Series -- 2.4 Simple Autoregressive Models -- 2.4.1 Properties of AR Models -- 2.4.2 Identifying Ar Models in Practice -- 2.4.3 Goodness of Fit -- 2.4.4 Forecasting -- 2.5 Simple Moving Average Models -- 2.5.1 Properties of MA Models -- 2.5.2 Identifying MA Order -- 2.5.3 Estimation -- 2.5.4 Forecasting Using MA Models -- 2.6 Simple Arma Models -- 2.6.1 Properties of ARMA(1,1) Models -- 2.6.2 General ARMA Models -- 2.6.3 Identifying ARMA Models -- 2.6.4 Forecasting Using an ARMA Model -- 2.6.5 Three Model Representations for an ARMA Model -- 2.7 Unit-root Nonstationarity -- 2.7.1 Random Walk -- 2.7.2 Random Walk with Drift -- 2.7.3 Trend-stationary Time Series -- 2.7.4 General Unit-root Nonstationary Models -- 2.7.5 Unit-root Test -- 2.8 Exponential Smoothing -- 2.9 Seasonal Models -- 2.9.1 Seasonal Differencing -- 2.9.2 Multiplicative Seasonal Models -- 2.9.3 Seasonal Dummy Variable -- 2.10 Regression Models with Time Series Errors -- 2.11 Long-memory Models…”

“…Justice in the U.S.S.R. Spanish…”