Maßzahl für die Güte die Visualisierung. Für die Transposons, die sich in den Sequenzdaten des Genomes Project verborgen. MATLAB and SAGA, in: SAGA – Seconds Out, Hamburger Beiträge Zur Physischen GIS- und fernerkundungsgestützte Analyse und Visualisierung von Klima-.
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Die Plots sind interaktiv und mit Brushing und Identification verbunden. Die XAusgabe befindet sich im Paket gnuplot-x It uses real-space uniform grids and multigrid methods, atom-centered basis-functions or plane-waves. Es umfasst z. Graphzeichnen widmet sich dem Problem der Visualisierung von strukturierter Information durch konstruieren geometrischer Darstellungen von abstrakten Graphen und Netzwerken.
The routines are written from scratch by the GSL team in C, and present a modern API for C programmers, while allowing wrappers to be written for very high level languages. Dieses Paket beinhaltet das Hauptprogramm und seine Module.
Es ist ein sehr umfassendes und angesehenes Paket, das umfangreiche Verwendung in der wissenschaftlichen Gemeinschaft gefunden hat. Die Merkmale:. MayaVi2 wurde hinsichtlich Skriptschnittstelle und Erweiterbarkeit konzipiert. It efficiently supports different computation and communication platforms including commodity clusters, SMPs, massively parallel systems, and high-speed networks.
This release has all MPI 3. MPQC is an ab-inito quantum chemistry program. It is especially designed to compute molecules in a highly parallelized fashion. OpenMX Open source package for Material eXplorer is a program package for nano-scale material simulations based on density functional theories DFT , norm-conserving pseudopotentials and pseudo-atomic localized basis functions. Since the code is designed for the realization of large-scale ab initio calculations on parallel computers, it is anticipated that OpenMX can be a useful and powerful tool for nano-scale material sciences in a wide variety of systems such as biomaterials, carbon nanotubes, magnetic materials, and nanoscale conductors.
PSI3 is an ab-initio quantum chemistry program. It is especially designed to accurately compute properties of small to medium molecules using highly correlated techniques. The lmfit Python package provides a simple, flexible interface to non-linear optimization or curve fitting problems.
The package extends the optimization capabilities of scipy. The principal advantage of using Parameters instead of simple variables is that the objective function does not have to be rewritten to reflect every change of what is varied in the fit, or what relationships or constraints are placed on the Parameters.
This means a scientific programmer can write a general model that encapsulates the phenomenon to be optimized, and then allow user of that model to change what is varied and fixed, what range of values is acceptable for Parameters, and what constraints are placed on the model.
The ease with which the model can be changed also allows one to easily test the significance of certain Parameters in a fitting model. The lmfit package allows a choice of several optimization methods available from scipy. This method is by far the most tested and best support method in lmfit, and much of this document assumes this algorithm is used unless explicitly stated.
An important point for many scientific analysis is that this is only method that automatically estimates uncertainties and correlations between fitted variables from the covariance matrix calculated during the fit. In their native form, some of these methods setting allow upper or lower bounds on parameter variables, or adding constraints on fitted variables.
By using Parameter objects, lmfit allows bounds and constraints for all of these methods, and makes it easy to swap between methods without hanging the objective function or set of Parameters. Finally, because the approach derived from MINPACK-1 usin the covariance matrix to determine uncertainties is sometimes questioned and sometimes rightly so , lmfit supports methods to do a brute force search of the confidence intervals and correlations for sets of parameters.
Pyxplot is a multi-purpose graph plotting tool, scientific scripting language, vector graphics suite, and data processing package. Its interface is designed to make common tasks -- e. Pyxplot produces publication-quality figures. To this end, text is rendered with all of the beauty and flexibility of the LaTeX typesetting environment.
Extensive documentation and examples can be found in the pyxplot-doc package. A gallery of sample plots is available from the project's web site. It is based on density-functional theory, plane waves, and pseudopotentials both norm-conserving, ultrasoft, and PAW. It fits analytic functions describing different types of material microstructure to experimental data in order to determine the shape, size and degree of ordering.
Scilab ist ein matrixorientiertes, wissenschaftliches Softwarepaket. MathGL kann ebenso auf der Konsole verwendet werden. Es definiert eine stark typisierte, mathematisch korrekte Typenhierarchie. Es hat eine Programmiersprache und einen eingebauten Compiler. Axiom wird seit entwickelt und wurde als kommerzielles Produkt verkauft.
Data Explorer is a system of tools and user interfaces for visualizing data. In general terms the visualization of data can be considered a 3-stage process:. Provides some applications codes source and executables such as laplacian with cG and dG methods, stokes, heat transfer, solid mechanics static and dynamic.
Support the following basic entities: simplices segment, triangle, tetrahedron and product of simplices quadrangle, hexahedron. Support various point sets on these basic entities: equispaced points, quadrature points, interpolation points Gauss-Lobatto, Fekete, WarpBlend?
Provide mathematical concept for higher order abstraction Function spaces and associated elements, forms and operators. This is a technique to obtain the velocity field of a fluid flow quantitatively and is performed by tracking tracer particles that have been seeded to a fluid.
The technique is also applied for observing deformations at surfaces of solid bodies. The package contains:. This module provides an interface to layout and image generation of directed and undirected graphs in a variety of formats PostScript, PNG, etc. Maxima beruht auf dem in den ern am MIT entwickelten Macsyma. Es ist mit Hunderten von Selbsttests ausgestattet. With Pygraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms.
Des weiteren ist der Task Mathematik-Wissenschaften empfohlen, um optionale Mathematik-Software zu installieren. FDMNES calculates the spectra of different spectroscopies related to the real or virtual absorption of x-ray in material. The calculation is performed with all conditions of rectilinear or circular polarization.
FDMNES also allows the comparison of the simulated spectra to experimental ones with the help of objective criteria. Octaviz is a visualization system for Octave. Octaviz also provides high-level functions for 2D and 3D visualization. Using those functions, most common visualization tasks 3D surface plots, contour plots etc can be accomplished without any knowledge about VTK.
It can be applied to all kinds of materials, irrespective of the atomic species involved, and also allows for the investigation of the atomic-core region. The computer program atompaw generates projector and basis functions which are needed for performing electronic structure calculations based on the Projector-Augmented Wave PAW method. The program is applicable to materials throughout the periodic table. Additional data files are also produced which can be used to help evaluate the accuracy and efficiency of the generated functions.
Wavelets constitute a real space basis set distributed on an adaptive mesh two levels of resolution in our implementation. Thanks to our Poisson solver based on a Green function formalism, periodic systems, surfaces and isolated systems can be simulated with the proper boundary conditions. The Levenberg-Marquardt LM algorithm is an iterative technique that finds a local minimum of a function that is expressed as the sum of squares of nonlinear functions.
It has become a standard technique for nonlinear least-squares problems and can be thought of as a combination of steepest descent and the Gauss-Newton method. Both unconstrained and constrained under linear equations, inequality and box constraints Levenberg-Marquardt variants are included. Octopus is a scientific program aimed at the ab initio virtual experimentation on a hopefully ever-increasing range of system types.
Nuclei are described classically as point particles. Electron-nucleus interaction is described within the pseudopotential approximation. This is an old version still used by several packages. Fix translated description. Description For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme: Official Debian packages with high relevance Official Debian packages with lower relevance Packaging has started and developers might try the packaging code in VCS Unofficial packages built by somebody else No known packages available but some record of interest WNPP bug No known packages available If you discover a project which looks like a good candidate for Debian Science to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Science mailing list.
Index of all tasks. Brain-computer interface. Data acquisition. Data acquisition development. Data management. Distributed computing. Financial engineering. High energy physics. High energy physics devel. Image analysis. Image analysis development. Machine learning. Nanoscale physics. Nanoscale physics development. Cognitive neuroscience. Neuroscience datasets. Modeling of neural systems. Numerical computation. Maintainer: Debichem Team Michael Banck.
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Caliste, R. Caracas, M. Deutsch, L. Genovese, Ph. Ghosez, M. Giantomassi, S. Goedecker, D. Hamann, P. Hermet, F. Jollet, G. Jomard, S. Leroux, M. Mancini, S. Mazevet, M. Oliveira, G. Onida, Y. Pouillon, T. Rangel, G. Rignanese, D. Sangalli, R. Shaltaf, M. Torrent, M. Verstraete, G. Zerah and J. Upload screenshot. Maintainer: Debichem Team Andrius Merkys. Popcon : 8 users 4 upd. Go tagging. Please cite: S. Bahn and K. Jacobsen: An object-oriented scripting interface to a legacy electronic structure code.
Maintainer: Debichem Team Drew Parsons. Popcon : 64 users 88 upd. Registry entries: Bio. Other screenshots of package avogadro Version URL 1. Popcon : 2 users 2 upd. Popcon : 9 users 2 upd. Popcon : 1 users 2 upd. Newer upstream! Popcon : 2 users 3 upd. Journal of Applied Crystallography 48 Popcon : 16 users 12 upd. Please cite: CP2K version 2. Maintainer: Debichem Team Daniel Leidert. Popcon : 14 users 4 upd. Please cite: Larry W. Finger, Martin Kroeker and Brian H. Toby: DRAWxtl, an open-source computer program to produce crystal-structure drawings.
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Popcon : 12 users 3 upd. Popcon : 16 users 9 upd. Popcon : 28 users 4 upd. Popcon : 24 users 8 upd. Please cite: P. Giannozzi, S. Data can be transferred from Excel to R and back. The clickable RExcel menu supplements the powerful R command language. Results from the analyses in R can be returned to the spreadsheet. Ordinary formulas in spreadsheet cells can use functions written in R. The book is accessible to readers with only a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods.
R code is provided throughout the text. Cowpertwait and Andrew Metcalfe. Introductory Time Series with R. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence confirms understanding of both the model and the R routine for fitting it to the data. Finally, the model is applied to an analysis of a historical data set. By using R, the whole procedure can be reproduced by the reader.
The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyze time series as part of their taught program or their research. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems.
Henry H. A Primer of Ecology with R. Starting with geometric growth and proceeding through stability of multispecies interactions and species-abundance distributions, this book demystifies and explains fundamental ideas in population and community ecology. Graduate students in ecology, along with upper division undergraduates and faculty, will all find this to be a useful overview of important topics.
Introduction to Multivariate Statistical Analysis in Chemometrics. It includes discussions of various statistical methods, such as principal component analysis, regression analysis, classification methods, and clustering. Written by a chemometrician and a statistician, the book reflects both the practical approach of chemometrics and the more formally oriented one of statistics. To enable a better understanding of the statistical methods, the authors apply them to real data examples from chemistry.
They also examine results of the different methods, comparing traditional approaches with their robust counterparts. In addition, the authors use the freely available R package to implement methods, encouraging readers to go through the examples and adapt the procedures to their own problems. Focusing on the practicality of the methods and the validity of the results, this book offers concise mathematical descriptions of many multivariate methods and employs graphical schemes to visualize key concepts.
It effectively imparts a basic understanding of how to apply statistical methods to multivariate scientific data. Broman and Saunak Sen. Two moderately challenging case studies illustrate QTL analysis in its entirety. Novice readers will find detailed explanations of the important statistical concepts and, through the extensive software illustrations, will be able to apply these concepts in their own research. Wiley-VCH, Requiring only little mathematical prerequisite in calculus and linear algebra, it is accessible to scientists, engineers, and students at the undergraduate level.
Bayesian Computation with R. Springer, 2nd edition, The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling.
Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner's g priors to choose between models in linear regression.
There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package.
Ramsay, Giles Hooker, and Spencer Graves. Functional Data Analysis with R and Matlab. Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems. It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase the functional data analysis.
The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. You will need some basic knowledge of R i. After reading this book you'll be able to produce graphics customized precisely for your problems, to and you'll find it easy to get graphics out of your head and on to the screen or page.
Computational Statistics. Includes bibliographical references and index. Integrating R code and examples throughout, the text only requires basic knowledge of statistics and computing. This introduction covers one-sample analysis and distribution diagnostics, regression, two-sample problems and comparison of distributions, and multivariate analysis. It uses a range of examples to demonstrate how R can be employed to tackle statistical problems.
In addition, the handy appendix includes a collection of R language elements and functions, serving as a quick reference and starting point to access the rich information that comes bundled with R. Accessible to a broad audience, this book explores key topics in data analysis, regression, statistical distributions, and multivariate statistics.
Full of examples and with a color insert, it helps readers become familiar with R. Dynamic Linear Models with R. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting.
All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed. Presses Universitaires de Rennes, Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or done simplistically, and updating of previously developed models is not considered.
A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. Clinical prediction models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model. These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formats.
The steps are illustrated with many small case-studies and R code, with data sets made available in the public domain. The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to updating of a model, and comparisons of centers after case-mix adjustment by a prediction model. The text is primarily intended for clinical epidemiologists and biostatisticians.
It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression.
The book is practical in nature. But it provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy. Prediction models are key to individualizing diagnostic and treatment decision making. Verlag Detlev Reymann, Geisenheim, Wright and Kamala London.
The authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders. Nonlinear Regression with R. Currently, R offers a wide range of functionality for nonlinear regression analysis, but the relevant functions, packages and documentation are scattered across the R environment.
This book provides a coherent and unified treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology. The book starts out giving a basic introduction to fitting nonlinear regression models in R.
Subsequent chapters explain the salient features of the main fitting function nls , the use of model diagnostics, how to deal with various model departures, and carry out hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered. Foulkes elucidates core concepts that undergird the wide range of analytic techniques and software tools for the analysis of data derived from population-based genetic investigations.
Applied Statistical Genetics with R offers a clear and cogent presentation of several fundamental statistical approaches that researchers from multiple disciplines, including medicine, public health, epidemiology, statistics and computer science, will find useful in exploring this emerging field. Ieno, Neil Walker, Anatoly A. Saveiliev, and Graham M. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout.
The second part provides ten case studies that range from koalas to deep sea research. These chapters provide an invaluable insight into analysing complex ecological datasets, including comparisons of different approaches to the same problem. By matching ecological questions and data structure to a case study, these chapters provide an excellent starting point to analysing your own data.
Zuur, Elena N. Ieno, and Erik Meesters. A Beginner's Guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R. The book should be useful to practitioners and students with minimal mathematical background, but because of the many R programs, probably also to many mathematically well educated practitioners.
Many of the methods presented in the book have, so far, not been used much in practice because the lack of an implementation in a unified framework. This book fills the gap. With the R code included in this book, a lot of useful methods become easy to use for practitioners and students. Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations.
The book is organized in four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other milestones publication known so far.
The third one is focused on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation.
The reader non-expert in R language, will find a concise introduction to this environment focused on the subject of the book which should allow for instant use of the proposed material. To each R functions presented in the book a documentation page is available at the end of the book. A Modern Approach to Regression with R. When weaknesses in the model are identified, the next step is to address each of these weaknesses.
A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. The regression output and plots that appear throughout the book have been generated using R. On the book website you will find the R code used in each example in the text. The book contains a number of new real data sets from applications ranging from rating restaurants, rating wines, predicting newspaper circulation and magazine revenue, comparing the performance of NFL kickers, and comparing finalists in the Miss America pageant across states.
One of the aspects of the book that sets it apart from many other regression books is that complete details are provided for each example. The book is aimed at first year graduate students in statistics and could also be used for a senior undergraduate class.
Lattice: Multivariate Data Visualization with R. Lattice brings the proven design of Trellis graphics originally developed for S by William S. Cleveland and colleagues at Bell Labs to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research.
Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages.
All code and figures in the book are also available online, along with supplementary material covering more advanced topics. Bivand, Edzer J. Applied Spatial Data Analysis with R. This part is of interest to users who need to access and visualise spatial data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping.
The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website.
This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science.
Peng and Francesca Dominici. The methods and software developed in this area are applicable to a wide array of problems in environmental epidemiology. This book provides an overview of the methods used for investigating the health effects of air pollution and gives examples and case studies in R which demonstrate the application of those methods to real data. The book will be useful to statisticians, epidemiologists, and graduate students working in the area of air pollution and health and others analyzing similar data.
The authors describe the different existing approaches to statistical modeling and cover basic aspects of analyzing and understanding air pollution and health data. The case studies in each chapter demonstrate how to use R to apply and interpret different statistical models and to explore the effects of potential confounding factors.
A working knowledge of R and regression modeling is assumed. In-depth knowledge of R programming is not required to understand and run the examples. Software for all of the analyses in the book is downloadable from the web and is available under a Free Software license.
The reader is free to run the examples in the book and modify the code to suit their needs. With the database, readers can run the examples and experiment with their own methods and ideas. Bioinformatics with R. R Programming for Bioinformatics. R Programming for Bioinformatics builds the programming skills needed to use R for solving bioinformatics and computational biology problems.
Drawing on the author's experiences as an R expert, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages.
He concludes with a discussion on the debugging and profiling of R code. Data Manipulation with R. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R.
Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks.
Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R.
For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book. Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions.
Springer, New York, 2nd edition, This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R.
The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models. The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes. The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting.
The author explains how these model types relate to each other. He obtained a diploma and a doctorate degree at the economics department of the latter entity where he was employed as a research and teaching assistant. Introductory Statistics with R. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets.
The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations.
In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression. Statistical Computing with R. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts.
Semiparametric Regression for the Social Sciences. Semiparametric Regression for the Social Sciences sets out to address this situation by providing an accessible introduction to the subject, filled with examples drawn from the social and political sciences. Readers are introduced to the principles of nonparametric smoothing and to a wide variety of smoothing methods. The author also explains how smoothing methods can be incorporated into parametric linear and generalized linear models.
The use of smoothers with these standard statistical models allows the estimation of more flexible functional forms whilst retaining the interpretability of parametric models. The full potential of these techniques is highlighted via the use of detailed empirical examples drawn from the social and political sciences. Each chapter features exercises to aid in the understanding of the methods and applications. All examples in the book were estimated in R. The book contains an appendix with R commands to introduce readers to estimating these models in R.
All the R code for the examples in the book are available from the author's website and the publishers website. Cryer and Kung-Sik Chan. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models.
All of the ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses.
Software for Data Analysis: Programming with R. This book guides the reader in programming with R, from interactive use and writing simple functions to the design of R packages and intersystem interfaces. World Scientific, Hackensack, NJ, It helps readers choose the best method from a wide array of tools and packages available. The data used in the examples along with R program snippets, illustrate the economic theory and sophisticated statistical methods extending the usual regression.
The R program snippets are included on a CD accompanying the book. These are not merely given as black boxes, but include detailed comments which help the reader better understand the software steps and use them as templates for possible extension and modification. The book has received endorsements from top econometricians. Wavelet Methods in Statistics with R. This book fulfils three purposes. First, it is a gentle introduction to wavelets and their uses in statistics. Second, it acts as a quick and broad reference to many recent developments in the area.
The book concentrates on describing the essential elements and provides comprehensive source material references. Third, the book intersperses R code that explains and demonstrates both wavelet and statistical methods. The code permits the user to learn the methods, to carry out their own analyses and further develop their own methods.
The book is designed to be read in conjunction with WaveThresh4, the freeware R package for wavelets. The book introduces the wavelet transform by starting with the simple Haar wavelet transform and then builds to consider more general wavelets such as the Daubechies compactly supported series.
The book then describes the evolution of wavelets in the directions of complex-valued wavelets, non-decimated transforms, multiple wavelets and wavelet packets as well as giving consideration to boundary conditions initialization. Later chapters explain the role of wavelets in nonparametric regression problems via a variety of techniques including thresholding, cross-validation, SURE, false-discovery rate and recent Bayesian methods, and also consider how to deal with correlated and non-Gaussian noise structures.
The book also looks at how nondecimated and packet transforms can improve performance. The penultimate chapter considers the role of wavelets in both stationary and non-stationary time series analysis. The final chapter describes recent work concerning the role of wavelets for variance stabilization for non-Gaussian intensity estimation.
The book is aimed at final year undergraduate and Masters students in a numerate discipline such as mathematics, statistics, physics, economics and engineering and would also suit as a quick reference for postgraduate or research level activity. The book would be ideal for a researcher to learn about wavelets, to learn how to use wavelet software and then to adapt the ideas for their own purposes.
This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences.
The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations geographic coordinates , which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.
The book is unique because it supplies direct access to software solutions based on R, the Open Source version of the S-language for statistics for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.
Morphometrics with R. The R language and environment offers a single platform to perform a multitude of analyses from the acquisition of data to the production of static and interactive graphs. This offers an ideal environment to analyze shape variation and shape change. This open-source language is accessible for novices and for experienced users. Adopting R gives the user and developer several advantages for performing morphometrics: evolvability, adaptability, interactivity, a single and comprehensive platform, possibility of interfacing with other languages and software, custom analyses, and graphs.
The book explains how to use R for morphometrics and provides a series of examples of codes and displays covering approaches ranging from traditional morphometrics to modern statistical shape analysis such as the analysis of landmark data, Thin Plate Splines, and Fourier analysis of outlines. The book fills two gaps: the gap between theoreticians and students by providing worked examples from the acquisition of data to analyses and hypothesis testing, and the gap between user and developers by providing and explaining codes for performing all the steps necessary for morphometrics rather than providing a manual for a given software or package.
Students and scientists interested in shape analysis can use the book as a reference for performing applied morphometrics, while prospective researchers will learn how to implement algorithms or interfacing R for new methods. In addition, adopting the R philosophy will enhance exchanges within and outside the morphometrics community. Julien Claude is evolutionary biologist and palaeontologist at the University of Montpellier 2 where he got his Ph.
He works on biodiversity and phenotypic evolution of a variety of organisms, especially vertebrates. He teaches evolutionary biology and biostatistics to undergraduate and graduate students and has developed several functions in R for the package APE. Applied Econometrics with R. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions.
In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research. It contains some data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e. The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care.
The goal of this book is to provide a guide to R for users with a background in economics or the social sciences. Readers are assumed to have a background in basic statistics and econometrics at the undergraduate level. A large number of examples should make the book of interest to graduate students, researchers and practitioners alike. Ecological Models and Data in R.
Princeton University Press, In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. The book shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results.
The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics. John Braun and Duncan J. Cambridge University Press, Cambridge, Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects.
The key feature of this book is that it covers models that are most commonly used in social science research-including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models-and it thoroughly develops each real-data example in painstaking detail.
Multiple Testing Procedures and Applications to Genomics. Statistical and Probabilistic Methods in Actuarial Science. It presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems. Correspondence Analysis in Practice, Second Edition. T his completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries.
It includes a computational appendix that provides the R commands that correspond to most of the analyses featured in the book. Data Analysis and Graphics Using R. Cambridge University Press, Cambridge, 2nd edition, There is extensive advice on practical data analysis. Topics covered include exploratory data analysis, tests and confidence intervals, regression, genralized linear models, survival analysis, time series, multi-level models, trees and random forests, classification, and ordination.
Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models.
Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. While R programs are provided on the book website and R hints are given in the computational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language.
Interactive and Dynamic Graphics for Data Analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models.
All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples. The Statistics of Gene Mapping. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses.
Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text. The author bases his approach on a framework of penalized regression splines, and builds a well- grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods.
The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Shumway and David S. Numerous examples using non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty.
The book is designed to be useful as a text for graduate level students in the physical, biological and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels.
Material from the earlier Prentice-Hall text Applied Statistical Time Series Analysis has been updated by adding modern developments involving categorical time sries analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods.
These add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models. The book is complemented by ofering accessibility, via the World Wide Web, to the data and an exploratory time series analysis program ASTSA for Windows that can be downloaded as Freeware.
Diggle and Paulo Justiniano Ribeiro. Model-based Geostatistics. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences.
Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics. Le and James V. It covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment.
It showcases non-stationary vector-valued processes, while treating stationarity as a special case. In particular, with members of their research group the authors developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes.
Furthermore they indicate new directions for development. Angewandte Statistik. Methodensammlung mit R. Springer, Berlin, Heidelberg, 12th completely revised edition, Das Programm R ist dabei ein leicht erlernbares und flexibel einzusetzendes Werkzeug, mit dem der Prozess der Datenanalyse nachvollziehbar verstanden und gestaltet werden kann. Diese The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics.
To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. Robust Statistical Methods with R. The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner.
Highlighting hands- on problem solving, many examples and computational algorithms using the R software supplement the discussion. The book examines the characteristics of robustness, estimators of real parameter, large sample properties, and goodness-of-fit tests. It also includes a brief overview of R in an appendix for those with little experience using the software.
Analysis of Phylogenetics and Evolution with R. Adopting R as a main tool for phylogenetic analyses sease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments.
The authors provide a concise introduction to R, including a summary of its most important features. They cover a variety of topics, such as simple inference, generalized linear models, multilevel models, longitudinal data, cluster analysis, principal components analysis, and discriminant analysis. With numerous figures and exercises, A Handbook of Statistical Analysis using R provides useful information for students as well as statisticians and data analysts.
Computational Genome Analysis: An Introduction. It focuses on com putational and statistical principles applied to genomes, and introduces the mat hematics and statistics that are crucial for understanding these applications. A ll computations are done with R. R Graphics. The power and flexibility of grid graphics. Building on top of the base or grid graphics: Trellis graphics and developing new graphics functions.
Using R for Introductory Statistics. It includes a large collection of exercises and numerous practical examples from a broad range of scientific disciplines. It comes complete with an online resource containing datasets, R functions, selected solutions to exercises, and updates to the latest features. It features a practical presentation of the theory with a range of applications from data mining, financial engineering, and the biosciences. The necessary R and S-Plus code is given for each analysis in the book, with any differences between the two highlighted.
Statistics for Biology and Health. Mase, T. Kamakura, M. Jimbo, and K. Introduction to Data Science for engineers Data analysis using free statistical software R in Japanese. Suuri-Kogaku-sha, Tokyo, April Heiberger and Burt Holland. Springer Texts in Statistics. Many of the displays appear here for the first time. Discusses construction and interpretation of graphs, principles of graphical design, and relation between graphs and traditional tabular results. Can serve as a graduate-level standalone statistics text and as a reference book for researchers.
In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. Concepts and techniques are illustrated with a variety of case studies. S functions are provided for each new graphical display format. All code, transcript and figure files are provided for readers to use as templates for their own analyses.
Linear Models with R. It clearly demonstrates the different methods available and in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion of topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results.
Statistik mit R. Statistical Tools for Nonlinear Regression. Laboratorio di statistica con R. McGraw-Hill, Milano, Garrett, Rafael A. Irizarry, and Scott L. The Analysis of Gene Expression Data. Venables and Brian D. Modern Applied Statistics with S. Fourth Edition. In the first chapters it gives an introduction to the S language. Then it covers a wide range of statistical methodology, including linear and generalized linear models, non-linear and smooth regression, tree-based methods, random and mixed effects, exploratory multivariate analysis, classification, survival analysis, time series analysis, spatial statistics, and optimization.
It introduces S, and concentrates on how to use linear and generalized-linear models in S while assuming familiarity with the statistical methodology. Javier de Cos Juez, and Fco. Control de Calidad. Servicio de Publicaciones de la Universidad de La Rioja, It combines the theoretical basis with applied examples coded in R.
This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies. For example, there are missing data in the majority of datasets one is likely to encounter other than those used in textbooks! S Programming. Therneau and Patricia M. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Pinheiro and Douglas M.
Programming with Data. Chambers and Trevor J. Statistical Models in S. It described software for statistical modeling in S and introduced the S3 version of classes and methods. Becker, John M. Chambers, and Allan R. The New S Language. This file was generated by bibtex2html 1.
Dan E. Jean-Francois Mas. Thomas Rahlf. Steven Murray. Lawrence Leemis. Vikram Dayal. Matthias Kohl. Marta Blangiardo and Michela Cameletti. Victor A. Torsten Hothorn and Brian S. Sarah Stowell. Ruey S. Michael J. Lise Bellanger and Richard Tomassone. Yvonnick Noel. Yihui Xie. Robert J Knell.
Joseph Hilbe. Christopher Gandrud. Dirk Eddelbuettel. Din Chen. Stano Pekar and Marek Brabec. Bernhard Pfaff. David Lunn. Michael Lawrence. Dimitris Rizopoulos. Brian Dennis. Pierre-Andre Cornillon. Yves Aragon. Paul Teetor. Paul Murrell. Laura Chihara and Tim Hesterberg. John Fox and Sanford Weisberg.
Hrishi Mittal. Graham Williams. Bruno Falissard. Randall L. Claus Thorn Ekstrom. James Michael Curran. Christian P. Chris Hay Jahans. Damon M. Shravan Vasishth and Michael Broe. Robert A. Rob Kabacoff. Joseph Adler. John M. David Ruppert. Christian Robert and George Casella. Carlo Gaetan and Xavier Guyon. Andrew P. Hrishikesh D. Victor Bloomfield.
Since R is increasingly used in bioinformatics applications such as the BioConductor project, it can serve students as their basic quantitative, statistical, and graphics tool as they develop their careers. Uwe Ligges. Richard M. Peter D. Paul S. Kurt Varmuza and Peter Filzmoser. Karl W. Kai Velten.
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