Getting Started | Basic Functionality | Organization of CHNOSZ | Functions without side effects (return values) | Functions with side effects (modify system state) | Querying the thermodynamic database | Calculating thermodynamic properties | Working with reactions | Chemical affinity and stability diagrams | Equilibrium calculations | Activity coefficients | References | Interlude: Affinity, Formation Reactions, and Balancing | Caution: Quasisolubility contours on predominance diagrams underestimate total solubility | Advanced Uses | 1. Use helper functions to create formatted labels for diagrams | 2. Use retrieve() to search species by elements | 3. Load optional data with add.OBIGT() | 4. Use OBIGT() and reset() to restore the default database and settings | 5. Use basis() species to define the compositional space | 6. Set activities of formed species() to define a quasisolubility contour | 7. When to use add = TRUE | 8. Set grid resolution and constant T, P, or ionic strength in affinity() | 9. Use NaCl() to estimate ionic strength from NaCl concentration | 10. Use solubility() to draw total solubility contours | 11. Use convert() for common unit conversions | 12. Use the transect mode of affinity() for synchronized variables | 13. Choose non-default balancing constraints in diagram() | 14. Calculate adjusted and non-standard Gibbs energy with subcrt() | 15. Calculate non-standard Gibbs energy with affinity() | And swap basis species, remove formed species, and label reactions | 16. Extract results from the output of diagram() | 17. Writing chemical formulas and counting and summing elements with makeup() | 18: Accessing and changing settings with thermo() | Interlude: From Activity to Molality | Buffers | 1. Defining buffers with mod.buffer() | 2. Retrieving buffered activities with affinity(return.buffer = TRUE) | 3. Working with multiple buffered species (e.g., r h2s and r o2 in PPM) | 4. Using diagram(type = ) to display buffered activities | 5. Using fr o2 Buffers in downstream calculations | 6. Using buffer calculations in transects with affinity() | 7. Calculating the neutral pH of water | 8. Working with mineral pH buffers | Comprehensive example: Ore formation environments | Proteins | 1. Identifying proteins | 2. Adding proteins from FASTA or CSV files | 3. Calculating protein properties | Protein length and formula | Carbon oxidation state | 4. Thermodynamic calculations for proteins | Standard thermodynamic properties | Ionization effects | 5. Setting up a chemical system with proteins | 6. Calculating affinities and equilibrium distributions | Affinities of formation reactions | Equilibrium distributions | Scaling protein abundances | 7. Additional protein analysis | End-to-end example: Parameter optimization to fit experimental protein abundances | Environmental controls on protein evolution: A case study with CRISPR-Cas systems | Further Resources - Demos | More use cases for mosaic() | Solubility contours with solubility() | Other contour plots | Calculations using the output of diagram() | Activity buffers | Other thermodynamic models | Calculations with proteins | Further Resources - Vignettes | Frequently asked questions | OBIGT thermodynamic database | Customizing the thermodynamic database | Fitting thermodynamic data | Creating multi-metal diagrams | Getting Help | Document History

Where do the names CHNOSZ, OBIGT, and subcrt come from? | How should CHNOSZ be cited? | What thermodynamic models are used in CHNOSZ? | What are the main limitations of CHNOSZ? | When and why do equal-activity boundaries depend on total activity? | Set up subplots | Activate DEW model | logfO2-pH diagram for aqueous inorganic and organic carbon species at high pressure | After Figure 1b of Sverjensky et al., 2014b [SSH14] | (Nature Geoscience, https://doi.org/10.1038/NGEO2291) | Define system | A function to make the diagrams | Set total C concentration to 0.03 molal | (from EQ3NR model for eclogite [Supporting Information of SSH14]) | Restore default settings for the questions below | How can minerals with polymorphic transitions be added to the database? | How can I make a diagram with the trisulfur radical ion (r S3minus)? | Why does the published diagram have a much larger stability field for r S3minus? | Can I make the diagram using the Deep Earth Water (DEW) model? | In OBIGT, what is the meaning of T for solids, liquids, and gases? | How can mineral pH buffers be plotted? | Why are mineral stability boundaries curved on mosaic diagrams? | Get the pKa of H2S (note the minus sign!) | Reaction 1 between pyrite and pyrrhotite with H2S | Reaction 1 law of mass action (LMA) | logf_O2 = 2 logK_1 - 2 loga_H2S | Reaction 2 between pyrite and pyrrhotite with HS- | Reaction 2 LMA | logf_O2 = 2 pH + 2 logK_2 - 2 loga_HS- | The logf_O2 for each reaction is the same at the pKa of H2S | How is the sum of activities of basis species defined for mosaic diagrams? | The basis species we are speciating | The basis species defining the system | Note 1: for mosaic(), the first species in 'bases' must be in this list | Note 2: for solubility(), the first species in this list must contain S | Define a low fO2 so reduced sulfur dominates over most of the pH range | The pH range we'll look at | The logarithm of activity used for aH2S or sum(S) | Left-hand plot: Constant log aH2S, variable sum(S) | Set it up as a solubility calculation | Define a single log aH2S | Specify the aqueous species in equilibrium with H2S | Run the calculation to calculate all species' activities | Diagram individual activities then total activity | Right-hand plot: Constant sum(S), variable log aH2S | Start by loading all the candidate species with preset activities | Calculate affinities of formation reactions | Equilibrate the species for a total activity of S | Look at the stabilities of calcite and anhydrite | For afffinity(), these are single activities | For mosaic(), these are total activities for groups of species in the 'bases' list | affinity() calculation | mosaic() calculation | References

Mashing | Mixing 1 | Convert formation energies from eV / atom to eV / molecule | Convert formation energies from eV / molecule to J / mol | Gibbs energies of formation (J / mol) for aqueous species | Most are from Wagman et al., 1982 | Gibbs energies of formation (J / mol) for solids from Wagman et al., 1982 | Calculate correction for difference between reference and DFT energies (Persson et al., 2012) | Apply correction to standard Gibbs energies of aqueous species (Persson et al., 2012) | Add energies to OBIGT | This function modifies OBIGT and returns the species indices of the affected species | We explicitly set the units to Joules (this is the default as of CHNOSZ 2.0.0) | We need model = "CGL" to override the Berman model for some minerals 20220919 | Formation energies (eV / atom) for bimetallic solids from Materials API | mp-1335, mp-1079399, mp-866134, mp-558525, mp-504509 (triclinic FeVO4) | Convert energies and add to OBIGT | Mosaic Stacking 1 | Mosaic Stacking 2 | Setup basis species | Fe-bearing minerals | Add aqueous species 20210220 | Start plot with just the fields for transparency effect | Cu-bearing minerals | Mosaic with all Fe species as basis species | Use only predominant Fe species as basis species (to speed up calculation) 20210224 | Use loga_aq argument to control the activity of aqueous species in mosaic calculation 20220722 | c(NA, logm_aq) means to use: | basis()'s value for logact of aqueous S species | logm_aq for logact of aqueous Fe species | Adjust labels | Highlight Ccp and Bn in orange | Thick line around Ccp field | Add second Cu label | Plot the Fe-system lines and names "on top" so they are not covered by fill colors | Restore default OBIGT database | Mixing 2 | Mosaic Stacking 3 | Fe-S-O-H diagram | Order species by a function of composition to make colors | Cu-Fe-S-O-H diagram based on reactions with the | stable Fe-bearing minerals (mosaic stack) | Mash the diagrams and adjust labels | Function to calculate solubility of Cu for stable assemblages of Fe and Cu minerals | (i.e. equilibrium is imposed with all of these minerals, not only Cu(s)) | DIAGRAM 1 | Load SLOP98 data to use legacy parameters for CuCl2- and CuCl3-2 | Calculate logK for CuCl2- dissociation at 125 °C | Sverjensky (1987) used Helgeson (1969) value, which is ca. -5.2 | Calculate the difference in ΔG° corresponding to this logK difference | We should use calories here because the database values are in calories 20220604 | Apply this difference to the CuCl2- entry in OBIGT | Do the same thing for CuCl3-2 | DIAGRAM 2 | Set up system to dissolve S2(gas) | Calculate concentration of SO4-2 | DIAGRAM 3 | Secondary Balancing | PRIMARY balancing | Only Fe-bearing minerals | Only Cu-bearing minerals | Only Fe- AND Cu-bearing minerals | SECONDARY balancing | Fe- or Cu-bearing minerals | All minerals | Other Possibilities | Balancing on a Non-Metal | Mosaic Combo | A function to calculate Keff for any combination of T and pH | Make T and pH the same length | Calculate activity of H+ | Calculate logKs | Calculate Ks | Calculate Keff (Eq. 7) | Calculate logKeff as a function of pH at 100 °C | Calculate activity of acetamide for | acetic acid + acetate = 0.01 m | ammonia + ammonium = 0.001 m | METHOD 2: Mosaic combo | Define total activities | This is 2 * 0.01 because acetic acid has 2 carbons | Load all C-bearing species (including acetamide) | Calculate distribution of C-bearing species accounting for ammonia/ammonium speciation | Plot and label diagram | Start with empty diagram | Add pH = 6 line | Add line for acetamide activity calculated with Keff | Add lines from CHNOSZ calculations | Check that we got equal values | Document History | References

Aqueous Species H2O Inorganic Organic | Solids Inorganic Organic Berman | Gases Inorganic Organic    Liquids Inorganic Organic | Optional Data SUPCRT92 SLOP98 AD DEW Testing | References

Getting Started | Scatter Plots

Introduction | Preliminaries | LR and GLR-charts | Control-settings | Discussion

Introduction | Surveillance Data | Surveillance Algorithms | Discussion and Future Work | Acknowledgements

Model class | Data structure | Modeling and inference | Simulation

Introduction | Surveillance data | Model formulation | Function call and control settings | Conclusion

Introduction | Getting to know the basics of the package | Using surveillance in selected contexts | Implementing surveillance in routine monitoring | Discussion

Model class | Data structure | Modeling and inference | Simulation

Model class | Data structure | Modeling and inference | Simulation

Overview | Getting Data In | Agreement | Leik's Consensus | Tatsle and Wierman's Consensus | Berry and Mielke's IOV | Blair and Lacy's ``l'' (squared) | Garcia-Montalvo and Reynal-Querol | AJUS | ISD | (Multiple) Modes | Going Further

INTRODUCTION | A CONCEPTUAL LEGO SYSTEM | PLAYING LEGO | DISCUSSION

Objectives | Maximum Test | Illustration and Application | Simulation Experiments | Computational Details | Conclusions | Acknowledgements | Example Analyses

Introduction | Permutation Tests | Illustrations and Applications | Quality Assurance | Acknowledgements

Introduction | Permutation tests in a nutshell | A class structure for permutation tests | Interfaces to permutation inference | Permutation tests in practice: A categorical data example | Odds and ends | Expectation and covariance

Motivation | The model-based recursive partitioning algorithm | Illustrations | Conclusion

Introduction | Recursive binary partitioning | Recursive partitioning by conditional inference | Examples | Illustrations and applications

Simple Examples | Multiple Comparison Procedures | Two-way ANOVA | Test Procedures | Quality Assurance

Introduction | Model and Parameters | Global and Simultaneous Inference | Applications | Implementation | Illustrations | Conclusion

Ordinal regression: The cumulative Logit Model | Variable Selection according to Pollet and Nettle | Stepwise Backward Selection | Variable Selection by Simultaneous Inference

Classes and methods | Coercing tree objects | Growing a simple classification tree | Predictions | Conclusion

Overview | Introduction | Recursive binary partitioning | Recursive partitioning by conditional inference | Examples | Illustrations and applications | Backward compatibility and novel functionality

Overview | MOB: Model-based recursive partitioning | A new implementation in R | Illustrations | Setting up a new mobster | Conclusion

Overview | Motivating example | Technical details | Summary

Introduction | The zoo package | The survival package

The diagonal density of Frank's copula | Computing the ``diagonal MLE'' | Session Information | Conclusion

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features | Significance | Zero-Cycles (Connected Components) | One-Cycles (Loops) | Two-Cycles (Voids)

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features | Significance | Zero-Cycles (Connected Components) | One-Cycles (Loops) | Two-Cycles (Voids)

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features

Introduction | Archimedean copulas | Spearman's Rho for AMH

Introduction | Archimedean copulas | Nested Archimedean copulas | Outer power Archimedean copulas | Session Information | Conclusion

1. I know that duplicate times are not allowed but my data has them. What do I do? | 2. When I try to specify a log axis to plot.zoo a warning is issued. What is wrong? | 3. How do I create right and a left vertical axes in plot.zoo? | 4. I have data frame with both numeric and factor columns. How do I convert that to a "zoo" object? | 5. Why does lag give slightly different results on a "zoo" and a "zooreg" series which are otherwise the same? | 6. How do I subtract the mean of each month from a "zoo" series? | 7. How do I create a monthly series but still keep track of the dates? | 8. How are axes added to a plot created using plot.zoo? | 9. Why is nothing plotted except axes when I plot an object with many NAs? | 10. Does zoo work with Rmetrics? | 11. What other packages use zoo? | 12. Why does ifelse not work as I expect? | 13. In a series which is regular except for a few missing times or for which we wish to align to a grid how is it filled in or aligned? | 14. What is the difference between as.Date in zoo and as.Date in the core of R? | 15. How can I speed up zoo?

Introduction | Heckman's solution | Implementation | Usage | Two replication exercises | Robustness issues | Conclusions

Introduction | Installation | Retrieving data structure from GPML file | Edges | Nodes | Groups | Anchors | Converting Pathways into igraph objects | Bundling the Process | Finding cycles

Introduction | Package `gsl' | Session Info

title: "VarSelLCM"author: "Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values"date: "r Sys.Date()"output: rmarkdown::html_vignettevignette: >%\VignetteIndexEntry | Introduction | Mixed-type data analysis | Clustering | Shiny application

Introduction | Setting | Simulation | Simulation Results | Maximum Asymptotic Bias | Session Information

Multiple Correspondence Analysis in a Nutshell | Mixed Input Data

Ordinal PCA in a Nutshell | Standard PCA with Gifi | Mixed PCA

Theory in a Nutshell | Implementation

Linear Regression | Polynomial Regression | Piecewise Linear Regression | Spline Regression | Monotone Regression | Nominal Transformation | Cross-Validation | Multiple Linear Regression | Multiple Ordinal Regression

Introduction | Beta regression | Implementation in R | Beta regression in practice | The basic model: Estimation, inference, diagnostics | Prater's gasoline yield data | Household food expenditures | Variable dispersion model | Selection of different link functions | Further replication exercises | Dyslexia and IQ predicting reading accuracy | Structural change testing in beta regressions | Summary | Acknowledgments | References

Introduction | Matrix Classes | More detailed examples of ``Matrix'' operations | Notes about S4 classes and methods implementation | Session Info

Introduction | Loader's Binomial Deviance D0(x,M) = bd0(x, M) | Accuracy of log1pmx(x) Computations | Accuracy of p1l1(t) Computations | Accuracy of stirlerr(x)=delta(x) Computations

Basic structure of OBIGT | Conventions for data entry in OBIGT | Types of data | Ranges of HKF and CGL models | Required and optional data | NA or 0? | OOM scaling and r info_ | Case study: NA and 0 in the default database | Examples of adding data from a file | r add.OBIGT_ with optional data files | r add.OBIGT_ with other CSV files | Examples of adding and modifying data with a function | r mod.OBIGT_ for aqueous species | r mod.OBIGT_ for minerals | Case study: Formation constants for aqueous tungsten species | Fitting formation constants | Diagram 1: Constant molality of r F_ | Diagram 2: Variable molality of r F_ | References

A note on the equations | A note on the algorithms | An example for neutral species | Setting the value of omega | An example for charged species | Doing it for volume | Making a pseudospecies: r h4sio4 | References

1 Swiss Alps copulas of Hofert, Vrins (2013) | Lambda and its inverse | M and its inverse (for $M_i, i=1,2$): | S and its inverse (for $S_i, i=1,2$) | Wrappers for $p_1$ and $p_2$ and their inverses: | Define the copula $C$ | Draw n vectors of random variates from $C$ | 2 An example from Wolfgang Trutschnig and Manuela Schreyer | Define the Iterated Function System | Run chaos game B times | 3 Sierpinski tetrahedron | Session information

Generating the data | Estimation under unknown marginal parameters | Plot some true and estimated conditional quantile functions

Introduction | Data Preparation | Computing graph metrics | Degrees and Hubs

Introduction | Data Preparation | Plots

Read a series from a text file | Query dates | Convert back into a plain matrix | Union and intersection | Visualization | Select (a few) observations | Handle missing data | Prices and returns | Query Yahoo! Finance | Summaries

Introduction | The class "zoo" and its methods | Combining zoo with other packages | Summary and outlook | Reference card

Introduction | Regression models | Censored regression (tobit) | Truncated regression | R implementation | Example | Summary | References

Introduction | A brief review of beta regression | Implementation in R | Extensions | Bias correction and reduction in beta regressions | Preamble | Generic framework | Bias correction and bias reduction for beta regressions | Implementation in betareg | Beta regression trees | Finite mixtures of beta regressions | Illustrative application | Bias correction and reduction | Beta regression tree | Latent class beta regression | Conclusions | Acknowledgments | Appendix: Bias correction/reduction for gasoline yield data | References

Automatic generation by arithmetics and the like | Using generating functions | Doing it from scratch | Help needed / collaboration welcome

Introduction | Specifying Transformation Models | Most Likely Transformations | Transformation Analysis | Classical Likelihood Inference | Simulation-based Likelihood Inference | Summary | The variables Package | The basefun Package | The mlt Package

Introduction | Overview of random generation algorithms | Examples of distinguishing from truly random numbers | Description of the random generation functions | Random generation tests | Description of RNG test functions | Calling the functions from other packages

The runif interface | Dedicated functions

Introduction | GNEP as a nonsmooth equation | Notation and definitions | A classic example | Usage example | Localization of the GNEs | Benchmark of the complementarity functions and the computation methods | Initial point $z_0 = (4, -4, 1, 1)$ | Initial point $z_0 = (-4, 4, 1, 1)$ | Initial point $z_0 = (3, 0, 1, 1)$ | Initial point $z_0 = (0, 3, 1, 1)$ | Initial point $z_0 = (-1, -1, 1, 1)$ | Initial point $z_0 = (0, 0, 1, 1)$ | Conclusions | Special case of shared constraints with common multipliers | Constrained-equation reformulation of the KKT system | GNEP as a fixed point equation or a minimization problem | NI reformulation | QVI reformulation | The jointly convex case | In this subsection, we present reformulations for a subclass of GNEP called jointly convex case.Firstly, the jointly convex setting requires that the constraint function is common to all players $g^1=\dots =g^N= g$.Then, we assume, there exists a closed convex subset $X \subset \R^n$ such that for all player $i$,$$ | NIF formulation for the jointly convex case | QVI formulation for the jointly convex case | List of examples | Tables for the nonsmooth reformulation | Appendix for the nonsmooth reformulation | \subsection{Semismooth reformulation -- Shared constraint case\label{app:ceq:jointcase}}The Jacobian is given by$$\Jac \widetilde H(x, \tilde \lambda, \tilde w) =\left(\begin{matrix}\Jac_x \bar L (x, \tilde \lambda) & \Jac_{\tilde \lambda} \bar L (x, \tilde \lambda) & 0 \\Jac_x \tilde g(x) & 0 & I \0 & \diag[\tilde w] & \diag[\tilde \lambda]\end{matrix}\right),$$where$$\Jac_{\tilde \lambda} \bar L (x, \tilde \lambda)

Introduction | MCD and ``the Fast'' MCD (= fastmcd) Algorithm | Fast MCD Algorithm – General notation

Monotone psi-Functions | Redescenders

Overview | Installation | Choosing HCL-based color palettes | Usage with base graphics | Usage with ggplot2 | Palette visualization and assessment

Estimation | Per-phase variances | Raking

0. Set up required to run this tutorial | 1. Time and frequency analysis | 2. Correction of the significance level | 3. Saving the indices to an Excel spreadsheet | 4. Comparing more than two experimental groups | 5. Overwriting default parameters | 6. Nonlinear analysis

Area level and unit level models for estimating small area means | Preliminary | Area level models | Artificial poverty rate example | Spatial area level model | BRFSS diabetes rates | Unit level models | Corn and Soy Production | References

Introduction | Structure Learning | Covariate Adjustment | Random DAG Generation | General Object Handling | Appendix A: Simulation study

Input formats | Creating a DSM | The DSM parameters | Using DSM representations | Advanced techniques

title: "Introduction to Silly Putty"author: "Dwayne Tally, Zachary B. Abrams, and Kevin R. Coombes"date: "r Sys.Date()"output:rmarkdown::html_document:theme: journalhighlight: katevignette: >%\VignetteIndexEntry | Introduction | Setup | Generating and Formatting Data | Assign Umpire Model Parameters | Simulate Data | Euclidean Distance Matrix | Mercator Visualization | Different Clustering Methods | Hierarchical Clustering | Graphing Truth | PAM Clustering | SillyPutty Clustering | Combining SillyPutty With Hierarchical Clustering | Finding the Number of Clusters With SillyPutty | Appendix

Introduction | Pre-requisites | Using the package | More options | Further information | References

Introduction | Fitting a mixed-model tree with constant fits | Detecting treatment-subgroup interactions in clustered data | Detecting subgroups with different growth trajectories | R code for generating artificial motivating dataset

Linear least squares calculations

The Matrix class structures | Matrix Transformations | Session Info

One factor: y f1 | One factor, one continuous: y f1 + x | Two (or more) factors, main effects only: y f1 + f2 | Interactions of two (or more) factors,.....

Gaussian Quantiles in R | Correcting qnorm(., log.p=TRUE) | Fully accurate asymptotic qnorm(., log.p=TRUE) | Concluding summary | Computational details, session information | relErrV() (from sfsmisc) | p.qnormAsy2() for optimal cutpoints

Introduction | Arithmetic with mpfr-numbers | ``All'' mathematical functions, arbitrarily precise | Arbitrarily precise matrices and arrays | Special mathematical functions | Integration highly precisely | Miscellaneous | Conclusion

Introduction: Not log() nor exp(), but log1p() and expm1() | log1p() and expm1() for log(1 - exp(x)) | Computation of log(1+exp(x)) | Conclusion

Examples (sampling and evaluating the log-likelihood) | Example 1: ((1,2), (3,4,5))-Gumbel | Example 2: (1, (2,3), 4, (5,6,7))-Gumbel | Example 3: (1, (2,3))-Gumbel | Plots of the negative log-likelihood | Determine the values of the negative log-likelihood on a grid | Plotting | Computing the MLE (via optimization)

1 Auxiliary functions | 2 Checking the various (smoothed) empirical copulas | 3 Application to show non-uniqueness of Sklar's Theorem for Bernoulli margins

Archimedean-Simplex copulas | Liouville copulas | Archimedean-Liouville copulas

Setup and auxiliary functions | 1 ACs vs AXCs vs NACs vs (different) HAXCs | 1.1 AC (Clayton copula) | 1.2 AXC (Clayton frailties and Gumbel EVC) | 1.3 NAC (nested Clayton) | 1.4 HAXC (hierarchical Clayton frailties and Gumbel EVC) | 1.5 HAXC (hierarchical Clayton frailties and hierarchical Gumbel EVC, same hierarchical structure) | 1.6 HAXC (hierarchical Clayton frailties and hierarchical Gumbel EVC, different hierarchical structure) | 2 EVCs vs HEVCs vs (different) HAXCs | 2.1 EVC | 2.2 HEVC | 2.3 HAXC (Clayton frailty and HEVC) | 2.4 HAXC (hierarchical Clayton frailties and EVC) | 2.5 HAXC (hierarchical Clayton frailties and HEVC, same hierarchical structure) | 2.6 HAXC (hierarchical Clayton frailties and HEVC, different hierarchical structure)

1 Quasi-random numbers for copula models via conditional distribution method | Independence copula | Clayton copula | $t$ copula with three degrees of freedom | Marshall--Olkin copula | 3d $t$ copula with three degrees of freedom | 3d R-Vine copula | 2 Quasi-random numbers for copula models via stochastic representations | 2.1 Colorized scatter plot | Colorized scatter plot (quasi-random numbers and CDM) | Colorized scatter plots (quasi-random numbers and MO) | 2.2 A variance-reduction example

Introduction | Obtaining the package | Entering data | Descriptive statistics | Univariate approaches | A bivariate approach | Further development

Introduction | Statistical background | Source code | Using systemfit | Replication of textbook results | Summary and outlook | Object returned by systemfit | Computation times | Estimating systems of equations with sem

Introduction | An Introduction to the EVT | Basic Use | A Concrete Statistical Analysis of Peaks Over a Threshold | Dependence Models for Bivariate Extreme Value Distributions

Introduction | Setup | Extracting JSON data and formatting to LGF model | Extracting the cytoband locations, and the frequency data | Turning CytoData into an S4 Object | Generating Graphs | Plotting Cytoband Data Along the Genome | Plotting Cytoband-Level Data Along One Chromosome | Idiograms | One Data Column | More Data Columns | Gallery | Appendix

Introduction | Plotting Cytoband Data Along the Genome | Single Chromosome Plots | Plotting Cytoband-Level Data Along One Chromosome | Plotting Cytoband-Level Data Along Both Sides of One Chromosome | Idiograms | One Data Column | Contrasting Two Data Columns | Many Data Columns | Appendix

Introduction | The Mercator Class | UMAP | Euclideanization | Self-Organizing Maps | Warning | Conclusions | Appendix

Introduction | Non-central 2 probabilities: History of R's pnchisq.c

Introduction | Color palettes | Illustrations

Introduction | The BinaryMatrix Class | A Limited Sample Dataset | Generating the BinaryMatrix | Remove Duplicates | Data Filtering with Thresher | Visualization | Jaccard Distance | Hierarchical Clustering | t-Distributed Stochastic Neighbor Embedding | Multi-Dimensional Scaling | Silhouette-Width Barplots | iGraph | Cluster Identities | Sokal-Michener Metric | Changing the Color Palette | References

Getting Started | Simulating Complex Data | Plotting the results.

Introduction | Models | Estimation and inference | Prediction | Model assessment | Intervention analysis | Usage of the package | Comparison with other software packages | Outlook | Implementation details | Simulations

Introduction | The Mercator Class | Visualization | Hierarchical Clustering | t-Distributed Stochastic Neighbor Embedding | Multi-Dimensional Scaling | iGraph | Cluster Identities | Silhouette-Width Barplots | Reclustering | Hierarchical Clusters | True Clusters | Appendix

Introduction | Simulating Mixed-Type Clinical Data | Model Subtypes and Survival | Simulate Raw Data | Apply Clinically Realistic Noise | Simulate Mixed-Type Data | The MixedTypeEngine

Introduction | Gallery | Automatic formatting | Sanitization | Format examples | Suppressing printing | Acknowledgements | Session information

Introduction | Single Column Names | Multiple Column Names

Base Survival | Default method to generate beta coefficients. | Better method to generate beta coefficients. | Fewer possible hits | Appendix

1 Auxiliary functions | 2 Setup | Plot Kendall's tau as a function in $\theta$ for different $\nu$ | Parameter specification | 3 Sampling and estimation | Sampling | Estimation | 4 Plots | Profile likelihood plots | -log-likelihood plot | Wireframe | Levelplot

Introduction | Getting Started | Color Schemes | Row-by-row Matrix Operations | Color Coded Graphs

Getting Started | Paired Statistic | Alternate Inputs | Banked Statistic

The Problem | Treatment Effects with Spherical Disturbances | treatReg | Conclusion

Introduction | An Introduction to Max-Stable Processes | Unconditional Simulation of Random Fields | Spatial Dependence of Max-Stable Random Fields | Fitting a Unit Fréchet Max-Stable Process to Data | Model Selection | Fitting a Max-Stable Process to Data | Conclusion | P-splines with radial basis functions

Getting Started | Recommendations | A Small Palette | Do the seeds matter? | How many good colors can we find? | Improving the estimate | Computational Complexity

Getting Started | Resorted barplots | Scatter Plots | Plotting Curves | Color Similarity | Grayscale

Introduction | Red-Green Color Maps | Large Qualitative Palettes | Creating New Palettes | A Small Color-Safe Palette | Conclusion

Model Specification | Log-Likelihood Function | Restricting coefficients and 2 | Gradients of the CDF of the bivariate standard normal distribution | Gradients of the Log-Likelihood Function | Example with a Generated Dataset | Generate Dataset | Example with the `Smoke' dataset

1 Simulate data | 2 Fitting procedure based on the simulated data | 3 Simulate from the fitted time series model

Introduction | Simulated DataSet | Feature Selection | Fitting Models and Making Predictions

Introduction | Getting Started | Aligning Two Character Strings | Cell Line Names | Conclusions

Introduction | Getting Started | Processing in Pipelines

Installation | A 15-minutes guide to RHRV | Preprocessing the Heart Rate series | Load heart beat positions | Calculating HR and filtering | Interpolating | Plotting | Analysing the Heart Rate series | Time-domain analysis techniques\label | Frequency-domain analysis techniques | Fourier | Wavelets | Creating several analyses | A brief comparison: Wavelets Vs. Fourier

Intro | Auxiliary functions | Joe's family | Easy case ($\tau=0.2$) | Harder case ($d=150$, $\tau=0.3$) | Even harder case ($d=180$, $\tau=0.4$) | Gumbel's family | Harder case ($d=150$, $\tau=0.6$) | Frank's family (an already hard case) | Session information

1 Auxiliary functions | Margins | (Nested) Clayton Lévy copula | Plotting | 2 Sampling paths | 4d positive Clayton Lévy copula | 4d positive nested Clayton Lévy copula

Introduction

Introduction | Getting Started | Performing the Tail Rank Test | Power Computations | References

Introduction | Methods | Parameter selection | Data analysis | Graphical representation | Summary of breast cancer data results

Introduction | The basics of two-stage sampling | A-posteriori calculation of the alpha error | Risk based sampling | Designing sampling plans using the GUI | Sample size calculation using S4 classes | Sample size calculation without classes

A steady-state model of oxygen in a marine sediment | Global sensitivity analysis : Sensitivity ranges | Local sensitivity analysis : Sensitivity functions | Fitting the model to the data | Running a Markov chain Monte Carlo | Finally

The Example

Introduction | Getting Started | Gene-by-gene t-tests | Beta-uniform mixture models to account for multiple testing | Wilcoxon rank sum tests and empirical Bayes | Permutation based methods | Significance Analysis of Microarrays | Other class comparison approaches

Introduction | Getting Started | Distances and Clustering | Checking the Robustness of Clusters | Principal Components Analysis | Mosaics: red-green heatmaps | Class discovery with ExpressionSets

Simulated Data | PCA | A Second Round | A Final Round | Appendix

Introduction | The test model | Local sensitivity analysis | Multivariate parameter identifiability | Fitting the model to data | MCMC | Model prediction | Monte Carlo applications | Discussion | The Fortran version of the model

Introduction | The example model | Global sensitivity | Local sensitivity | Multivariate sensitivity analysis | Fitting the model to data | Markov chain Monte Carlo | Distributions | Examples | Finally

Introduction | Function modMCMC | Sampling from a normal distribution | Sampling from a lognormal distribution | The banana | A simple chemical model | Fitting a nonlinear model | Finally

Fitting a Monod function | Finally

Introduction | Classes for dense matrices | Classes for sparse matrices