| tigris | Load Census TIGER/Line Shapefiles into R |
| tikzDevice | R Graphics Output in LaTeX Format |
| tilegramsR | R Spatial Data for Tilegrams |
| tileHMM | Hidden Markov Models for ChIP-on-Chip Analysis |
| TileManager | Tile Manager |
| TilePlot | Characterization of functional genes in complex microbial communities using tiling DNA microarrays |
| tilting | Variable Selection via Tilted Correlation Screening Algorithm |
| time2event | Survival and Competing Risk Analyses with Time-to-Event Data as Covariates |
| timeDate | Rmetrics - Chronological and Calendar Objects |
| timedelay | Time Delay Estimation for Stochastic Time Series of Gravitationally Lensed Quasars |
| timekit | A Collection of Tools for Working with Time Series in R |
| timeline | Timelines for a Grammar of Graphics |
| timelineR | Visualization for Time Series Data |
| timelineS | Timeline and Time Duration-Related Tools |
| TimeMachine | Time Machine |
| timeordered | Time-ordered and time-aggregated network analyses |
| TimeProjection | Time Projections |
| timereg | Flexible Regression Models for Survival Data |
| timeROC | Time-Dependent ROC Curve and AUC for Censored Survival Data |
| timesboot | Bootstrap computations for time series objects |
| timeSeq | Detecting Differentially Expressed Genes in Time Course RNA-Seq Data |
| timeSeries | Rmetrics - Financial Time Series Objects |
| timeseriesdb | Manage Time Series with R and PostgreSQL |
| timetools | Seasonal/Sequential (Instants/Durations, Even or not) Time Series |
| timetree | Interface to the TimeTree of Life Webpage |
| timevis | Create Interactive Timeline Visualizations in R |
| TimeWarp | Date Calculations and Manipulation |
| timma | Target Inhibition Interaction using Maximization and Minimization Averaging |
| TIMP | Fitting Separable Nonlinear Models in Spectroscopy and Microscopy |
| timsac | Time Series Analysis and Control Package |
| Tinflex | Tinflex - Universal Non-Uniform Random Number Generator |
| TInPosition | Inference tests for TExPosition |
| tinsel | Transform Functions using Decorators |
| tint | Tint is not Tufte |
| TipDatingBeast | Using Tip Dates with Phylogenetic Trees in BEAST (Software for Phylogenetic Analysis) |
| tipom | Automated measure-based classification for flint tools |
| TippingPoint | Enhanced Tipping Point Displays the Results of Sensitivity Analyses for Missing Data |
| tis | Time Indexes and Time Indexed Series |
| titan | Titration analysis for mass spectrometry data |
| TITAN2 | Threshold Indicator Taxa Analysis |
| titanic | Titanic Passenger Survival Data Set |
| titrationCurves | Acid/Base, Complexation, Redox, and Precipitation Titration Curves |
| TKF | Pairwise Distance Estimation with TKF91 and TKF92 Model |
| tkrgl | TK widget tools for rgl package |
| tkrplot | TK Rplot |
| Tlasso | Non-Convex Optimization and Statistical Inference for Sparse Tensor Graphical Models |
| TLBC | Two-Level Behavior Classification |
| TLdating | Tools for Thermoluminescences Dating |
| tlemix | Trimmed Maximum Likelihood Estimation |
| tlm | Effects under Linear, Logistic and Poisson Regression Models with Transformed Variables |
| tlmec | Linear Student-t Mixed-Effects Models with Censored Data |
| TLMoments | Calculate TL-Moments and Convert Them to Distribution Parameters |
| tm | Text Mining Package |
| tm.plugin.alceste | Import texts from files in the Alceste format using the tm text mining framework |
| tm.plugin.dc | Text Mining Distributed Corpus Plug-In |
| tm.plugin.europresse | Import Articles from 'Europresse' Using the 'tm' Text Mining Framework |
| tm.plugin.factiva | Import Articles from 'Factiva' Using the 'tm' Text Mining Framework |
| tm.plugin.lexisnexis | Import Articles from 'LexisNexis' Using the 'tm' Text Mining Framework |
| tm.plugin.mail | Text Mining E-Mail Plug-In |
| tm.plugin.webmining | Retrieve Structured, Textual Data from Various Web Sources |
| tmap | Thematic Maps |
| tmaptools | Thematic Map Tools |
| TMB | Template Model Builder: A General Random Effect Tool Inspired by 'ADMB' |
| TMDb | Access to TMDb API - Apiary |
| tmg | Truncated Multivariate Gaussian Sampling |
| Tmisc | Turner Miscellaneous |
| tmle | Targeted Maximum Likelihood Estimation |
| tmle.npvi | Targeted Learning of a NP Importance of a Continuous Exposure |
| tmlenet | Targeted Maximum Likelihood Estimation for Network Data |
| tmod | Feature Set Enrichment Analysis for Metabolomics and Transcriptomics |
| tmpm | Trauma Mortality Prediction Model |
| tmvnsim | Truncated Multivariate Normal Simulation |
| tmvtnorm | Truncated Multivariate Normal and Student t Distribution |
| tnam | Temporal Network Autocorrelation Models (TNAM) |
| tnet | Software for Analysis of Weighted, Two-Mode, and Longitudinal Networks |
| Tnseq | Identification of Conditionally Essential Genes in Transposon Sequencing Studies |
| toaster | Big Data in-Database Analytics that Scales with Teradata Aster Distributed Platform |
| TOC | Total Operating Characteristic Curve and ROC Curve |
| tokenizers | A Consistent Interface to Tokenize Natural Language Text |
| tolBasis | Fundamental Definitions and Utilities of the Time Oriented Language (TOL) |
| tolerance | Statistical Tolerance Intervals and Regions |
| toOrdinal | Function for Converting Cardinal to Ordinal Numbers by Adding a Language Specific Ordinal Indicator to the Number |
| topicmodels | Topic Models |
| TopKLists | Inference, Aggregation and Visualization for Top-K Ranked Lists |
| topmodel | Implementation of the hydrological model TOPMODEL in R |
| topologyGSA | Gene Set Analysis Exploiting Pathway Topology |
| topsis | TOPSIS method for multiple-criteria decision making (MCDM) |
| tosls | Instrumental Variables Two Stage Least Squares estimation |
| TOSTER | Two One-Sided Tests (TOST) Equivalence Testing |
| TotalCopheneticIndex | Total Cophenetic Index |
| touch | Tools of Utilization and Cost in Healthcare |
| tourr | Implement Tour Methods in R Code |
| tourrGui | A Tour GUI using gWidgets |
| toxboot | Bootstrap Methods for 'ToxCast' High Throughput Screening Data |
| toxtestD | Experimental design for binary toxicity tests |
| TP.idm | Estimation of Transition Probabilities for the Illness-Death Model |
| tpAUC | Estimation and Inference of Two-Way pAUC, pAUC and pODC |
| tpe | Tree preserving embedding |
| TPEA | A Novel Topology-Based Pathway Enrichment Analysis Approach |
| TPmsm | Estimation of Transition Probabilities in Multistate Models |
| tpr | Temporal Process Regression |
| TR8 | A Tool for Downloading Functional Traits Data for Plant Species |
| tracer | Slick Call Stacks |
| tracheideR | Standardize Tracheidograms |
| track | Store Objects on Disk Automatically |
| trackdem | Particle Tracking and Demography |
| trackeR | Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices |
| TrackReconstruction | Reconstruct animal tracks from magnetometer, accelerometer, depth and optional speed data |
| tractor.base | Read, Manipulate and Visualise Magnetic Resonance Images |
| TRADER | Tree Ring Analysis of Disturbance Events in R |
| Trading | Trades, Curves, Rating Tables, Add-on Tables, CSAs |
| traitr | An interface for creating GUIs modeled in part after traits UI module for python |
| traits | Species Trait Data from Around the Web |
| Traitspace | A Predictive Model for Trait Based Community Assembly of Plant Species |
| traj | Trajectory Analysis |
| trajectories | Classes and Methods for Trajectory Data |
| TraMineR | Trajectory Miner: a Toolbox for Exploring and Rendering Sequences |
| TraMineRextras | TraMineR Extension |
| TRAMPR | 'TRFLP' Analysis and Matching Package for R |
| transcribeR | Automated Transcription of Audio Files Through the HP IDOL API |
| TransferEntropy | The Transfer Entropy Package |
| translate | Bindings for the Google Translate API v2 |
| translateR | Bindings for the Google and Microsoft Translation APIs |
| translateSPSS2R | Toolset for Translating SPSS-Syntax to R-Code |
| translation.ko | R Manuals Literally Translated in Korean |
| TransModel | Fit Linear Transformation Models for Right Censored Data |
| TransP | Implementation of Transportation Problem Algorithms |
| transport | Optimal Transport in Various Forms |
| tranSurv | Estimating a Survival Distribution in the Presence of Dependent Left Truncation and Right Censoring |
| trapezoid | The Trapezoidal Distribution |
| TRD | Transmission Ratio Distortion |
| TreatmentSelection | Evaluate Treatment Selection Biomarkers |
| treatSens | Sensitivity Analysis for Causal Inference |
| tree | Classification and Regression Trees |
| treebase | Discovery, Access and Manipulation of 'TreeBASE' Phylogenies |
| TreeBUGS | Hierarchical Multinomial Processing Tree Modeling |
| treeclim | Numerical Calibration of Proxy-Climate Relationships |
| treeClust | Cluster Distances Through Trees |
| treecm | Centre of Mass Assessment and Consolidation of Trees |
| treeHFM | Hidden Factor Graph Models |
| treelet | An Adaptive Multi-Scale Basis for High-Dimensional, Sparse and Unordered Data |
| treeman | Phylogenetic Tree Manipulation Class and Methods |
| treemap | Treemap Visualization |
| TreePar | Estimating birth and death rates based on phylogenies |
| treeperm | Exact and Asymptotic K Sample Permutation Test |
| treeplyr | 'dplyr' Functionality for Matched Tree and Data Objects |
| TreeSim | Simulating Phylogenetic Trees |
| TreeSimGM | Simulating Phylogenetic Trees under a General Model with or without Shifts |
| treespace | Statistical Exploration of Landscapes of Phylogenetic Trees |
| treethresh | Methods for Tree-Based Local Adaptive Thresholding |
| trelliscope | Create and Navigate Large Multi-Panel Visual Displays |
| trelloR | R API for Trello |
| trend | Non-Parametric Trend Tests and Change-Point Detection |
| TrendInTrend | Odds Ratio Estimation for the Trend in Trend Model |
| TriadSim | Simulating Triad Genomewide Genotypes |
| TrialSize | R functions in Chapter 3,4,6,7,9,10,11,12,14,15 |
| triangle | Provides the Standard Distribution Functions for the Triangle Distribution |
| triangulation | Determine Position of Observer |
| trib | Analysing and Visualizing Tribology Measurements |
| triebeard | 'Radix' Trees in 'Rcpp' |
| trifield | Some basic facilities for ternary fields and plots |
| TriMatch | Propensity Score Matching of Non-Binary Treatments |
| trimcluster | Cluster analysis with trimming |
| trimr | An Implementation of Common Response Time Trimming Methods |
| trimTrees | Trimmed opinion pools of trees in a random forest |
| trioGxE | A data smoothing approach to explore and test gene-environment interaction in case-parent trio data |
| trip | Tools for the Analysis of Animal Track Data |
| tripack | Triangulation of Irregularly Spaced Data |
| tripEstimation | Metropolis Sampler and Supporting Functions for Estimating Animal Movement from Archival Tags and Satellite Fixes |
| TripleR | Social Relation Model (SRM) Analyses for Single or Multiple Groups |
| TROM | Transcriptome Overlap Measure |
| TropFishR | Tropical Fisheries Analysis with R |
| trotter | Pseudo-Vectors Containing All Permutations, Combinations and Subsets of Objects Taken from a Vector |
| TRSbook | Functions and Datasets to Accompany the Book "The R Software: Fundamentals of Programming and Statistical Analysis" |
| trueskill | Implementation the TrueSkill algorithm in R |
| TruncatedNormal | Truncated Multivariate Normal |
| truncdist | Truncated Random Variables |
| truncgof | GoF tests allowing for left truncated data |
| truncnorm | Truncated normal distribution |
| truncreg | Truncated Gaussian Regression Models |
| truncSP | Semi-parametric estimators of truncated regression models |
| trust | Trust Region Optimization |
| trustOptim | Trust Region Optimization for Nonlinear Functions with Sparse Hessians |
| TSA | Time Series Analysis |
| tsallisqexp | Tsallis q-Exp Distribution |
| tsbridge | Calculate normalising constants for Bayesian time series models |
| tsBSS | Tools for Blind Source Separation for Time Series |
| tsbugs | Create time series BUGS models |
| tsc | Likelihood-ratio Tests for Two-Sample Comparisons |
| TSclust | Time Series Clustering Utilities |
| TScompare | 'TSdbi' Database Comparison |
| TSdata | 'TSdbi' Illustration |
| TSdbi | Time Series Database Interface |
| tsdecomp | Decomposition of Time Series Data |
| tsdisagg2 | Time Series Disaggregation |
| TSdist | Distance Measures for Time Series Data |
| tsDyn | Nonlinear Time Series Models with Regime Switching |
| tseries | Time Series Analysis and Computational Finance |
| tseriesChaos | Analysis of nonlinear time series |
| tseriesEntropy | Entropy Based Analysis and Tests for Time Series |
| TSeriesMMA | Multiscale Multifractal Analysis of Time Series Data |
| tsfa | Time Series Factor Analysis |
| TSfame | 'TSdbi' Extensions for Fame |
| TSGSIS | Two Stage-Grouped Sure Independence Screening |
| TSHRC | Two Stage Hazard Rate Comparison |
| tsintermittent | Intermittent Time Series Forecasting |
| tsiR | An Implementation of the TSIR Model |
| TSMining | Mining Univariate and Multivariate Motifs in Time-Series Data |
| TSmisc | 'TSdbi' Extensions to Wrap Miscellaneous Data Sources |
| TSMN | Truncated Scale Mixtures of Normal Distributions |
| tsModel | Time Series Modeling for Air Pollution and Health |
| TSMySQL | 'TSdbi' Extensions for 'MySQL' |
| tsna | Tools for Temporal Social Network Analysis |
| tsne | T-Distributed Stochastic Neighbor Embedding for R (t-SNE) |
| TSodbc | 'TSdbi' Extensions for ODBC |
| tsoutliers | Detection of Outliers in Time Series |
| TSP | Traveling Salesperson Problem (TSP) |
| Tsphere | Transposable Sphering for Large-Scale Inference with Correlated Data |
| tsPI | Improved Prediction Intervals for ARIMA Processes and Structural Time Series |
| tspmeta | Instance Feature Calculation and Evolutionary Instance Generation for the Traveling Salesman Problem |
| TSPostgreSQL | 'TSdbi' Extensions for 'PostgreSQL' |
| TSPred | Functions for Benchmarking Time Series Prediction |
| tsqn | Applications of the Qn Estimator to Time Series (Univariate and Multivariate) |
| TSS.RESTREND | Time Series Segmentation of Residual Trends |
| TSsdmx | 'TSdbi' Extension to Connect with 'SDMX' |
| tsSelect | Execution of Time Series Models |
| TSsql | Generic SQL Helper Functions for 'TSdbi' SQL Plugins |
| TSSQLite | 'TSdbi' Extensions for 'SQLite' |
| TSTr | Ternary Search Tree for Auto-Completion and Spell Checking |
| TSTutorial | Fitting and Predict Time Series Interactive Laboratory |
| tswge | Applied Time Series Analysis |
| tsxtreme | Bayesian Modelling of Extremal Dependence in Time Series |
| TTAinterfaceTrendAnalysis | Temporal Trend Analysis Graphical Interface |
| ttbbeer | US Beer Statistics from TTB |
| TTCA | Transcript Time Course Analysis |
| tth | TeX to HTML/MathML Translators tth/ttm |
| TTmoment | Sampling and Calculating the First and Second Moments for the Doubly Truncated Multivariate t Distribution |
| TTR | Technical Trading Rules |
| TTS | Master Curve Estimates Corresponding to Time-Temperature Superposition |
| ttScreening | Genome-wide DNA methylation sites screening by use of training and testing samples |
| tttplot | Time to Target Plot |
| ttutils | Utility functions |
| ttwa | Travel To Work Area |
| tuber | Client for the YouTube API |
| tubern | R Client for the YouTube Analytics and Reporting API |
| tuckerR.mmgg | Three-Mode Principal Components Analysis |
| tufte | Tufte's Styles for R Markdown Documents |
| tufterhandout | Tufte-style html document format for rmarkdown |
| TukeyC | Conventional Tukey Test |
| tumblR | Access to Tumblr v2 API |
| tumgr | Tumor Growth Rate Analysis |
| TunePareto | Multi-objective parameter tuning for classifiers |
| tuneR | Analysis of Music and Speech |
| tuple | Find every match, or orphan, duplicate, triplicate, or other replicated values |
| turboEM | A Suite of Convergence Acceleration Schemes for EM, MM and other fixed-point algorithms |
| turfR | TURF Analysis for R |
| turner | Turn vectors and lists of vectors into indexed structures |
| TurtleGraphics | Turtle graphics in R |
| tutorial | Convert R Markdown Files to DataCamp Light HTML Files |
| TUWmodel | Lumped Hydrological Model for Education Purposes |
| tvd | Total Variation Denoising |
| tvm | Time Value of Money Functions |
| twang | Toolkit for Weighting and Analysis of Nonequivalent Groups |
| tweedie | Tweedie Exponential Family Models |
| tweenr | Interpolate Data for Smooth Animations |
| tweet2r | Twitter Collector for R and Export to 'SQLite', 'postGIS' and 'GIS' Format |
| twiddler | Interactive manipulation of R expressions |
| twilio | An Interface to the Twilio API for R |
| R Based Twitter Client | |
| TwoCop | Nonparametric test of equality between two copulas |
| TwoPhaseInd | Estimate Gene-Treatment Interaction Exploiting Randomization |
| twoStageGwasPower | Compute thresholds and power for two-stage gwas |
| twostageTE | Two-Stage Threshold Estimation |
| TwoStepCLogit | Conditional Logistic Regression: A Two-Step Estimation Method |
| txtplot | Text based plots |
| types | Type Annotations |
| uaparserjs | Parse Browser 'User-Agent' Strings into Data Frames |
| UBCRM | Functions to Simulate and Conduct Dose-Escalation Phase I Studies |
| ubeR | Interface to the Uber API |
| UBL | An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks |
| ucbthesis | UC Berkeley graduate division thesis template |
| ucminf | General-Purpose Unconstrained Non-Linear Optimization |
| UCR.ColumnNames | Fixes Column Names for Uniform Crime Report "Offenses Known and Clearance by Arrest" Datasets |
| udapi | Urban Dictionary API Client |
| udunits2 | Udunits-2 Bindings for R |
| uHMM | Construct an Unsupervised Hidden Markov Model |
| uiucthemes | 'R' 'Markdown' Themes for 'UIUC' Documents and Presentations |
| ukds | Reproducible Data Retrieval from the UK Data Service |
| ukgasapi | API for UK Gas Market Information |
| Ultimixt | Bayesian Analysis of Location-Scale Mixture Models using a Weakly Informative Prior |
| ump | Uniformly Most Powerful Tests |
| umx | Structural Equation Modelling in R with 'OpenMx' |
| unbalanced | Racing for Unbalanced Methods Selection |
| unbalhaar | Function estimation via Unbalanced Haar wavelets |
| UncerIn2 | Implements Models of Uncertainty into the Interpolation Functions |
| UncertainInterval | Uncertain Area Methods for Cut-Point Determination in Tests |
| UNCLES | Unification of Clustering Results from Multiple Datasets using External Specifications |
| uncmbb | UNC Men's Basketball Match Results Since 1949-1950 Season |
| UNF | Tools for Creating Universal Numeric Fingerprints for Data |
| unfoldr | Stereological Unfolding for Spheroidal Particles |
| ungeneanno | Collate Gene Annotation Data from Uniprot and NIH Gene Databases |
| uniah | Unimodal Additive Hazards Model |
| Unicode | Unicode Data and Utilities |
| uniCox | Univarate shrinkage prediction in the Cox model |
| uniftest | Tests for Uniformity |
| uniqtag | Abbreviate Strings to Short, Unique Identifiers |
| uniqueAtomMat | Finding Unique or Duplicated Rows or Columns for Atomic Matrices |
| uniReg | Unimodal Penalized Spline Regression using B-Splines |
| unitedR | Assessment and Evaluation of Formations in United |
| unitizer | Interactive R Unit Tests |
| units | Measurement Units for R Vectors |
| unittest | TAP-Compliant Unit Testing |
| UnivRNG | Univariate Pseudo-Random Number Generation |
| unix | Unix System Utilities |
| unmarked | Models for Data from Unmarked Animals |
| unpivotr | Unpivot Complex and Irregular Data Layouts |
| unsystation | Stationarity Test Based on Unsystematic Sub-Sampling |
| untb | ecological drift under the UNTB |
| unvotes | United Nations General Assembly Voting Data |
| upclass | Updated Classification Methods using Unlabeled Data |
| uplift | Uplift Modeling |
| UPMASK | Unsupervised Photometric Membership Assignment in Stellar Clusters |
| upmfit | Unified Probability Model Fitting |
| UpSetR | A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets |
| uptimeRobot | Access the UptimeRobot Ping API |
| uqr | Unconditional Quantile Regression |
| urca | Unit Root and Cointegration Tests for Time Series Data |
| urlshorteneR | R Wrapper for the 'Bit.ly', 'Goo.gl' and 'Is.gd' URL Shortening Services |
| urltools | Vectorised Tools for URL Handling and Parsing |
| uroot | Unit Root Tests for Seasonal Time Series |
| USAboundaries | Historical and Contemporary Boundaries of the United States of America |
| UScancer | Create US cancer datasets from SEER, IARC, and US Census data |
| UScensus2000cdp | US Census 2000 Designated Places Shapefiles and Additional Demographic Data |
| UScensus2000tract | US Census 2000 Tract Level Shapefiles and Additional Demographic Data |
| UScensus2010 | US Census 2010 Suite of R Packages |
| usdm | Uncertainty Analysis for Species Distribution Models |
| usedist | Distance Matrix Utilities |
| useful | A Collection of Handy, Useful Functions |
| userfriendlyscience | Quantitative Analysis Made Accessible |
| USGSstates2k | Replaced by 'states2k' – United States of America Map with the NAD 1983 Albers Projection |
| UsingR | Data Sets, Etc. for the Text "Using R for Introductory Statistics", Second Edition |
| uskewFactors | Model-Based Clustering via Mixtures of Unrestricted Skew-t Sactor Analyzer Models |
| usl | Analyze System Scalability with the Universal Scalability Law |
| usmap | US Maps Including Alaska and Hawaii |
| UStatBookABSC | A Companion Package to the Book "U-Statistics, M-Estimation and Resampling" |
| ustyc | Fetch US Treasury yield curve data |
| utf8latex | Importing, Exporting and Converting Between Datasets and LaTeX |
| utility | Construct, Evaluate and Plot Value and Utility Functions |
| utiml | Utilities for Multi-Label Learning |
| uuid | Tools for generating and handling of UUIDs |
| UWHAM | Unbinned weighted histogram analysis method (UWHAM) |
| uwIntroStats | Descriptive Statistics, Inference, Regression, and Plotting in an Introductory Statistics Course |
| V8 | Embedded JavaScript Engine for R |
| vaersNDvax | Non-Domestic Vaccine Adverse Event Reporting System (VAERS) Vaccine Data for Present |
| vaersvax | US Vaccine Adverse Event Reporting System (VAERS) Vaccine Data for Present |
| valaddin | Functional Input Validation |
| validann | Validation Tools for Artificial Neural Networks |
| validate | Data Validation Infrastructure |
| validatejsonr | Validate JSON Against JSON Schemas |
| validateRS | One-Sided Multivariate Testing Procedures for Rating Systems |
| valorate | Velocity and Accuracy of the LOg-RAnk TEst |
| valottery | Results from the Virginia Lottery Draw Games |
| valr | Genome Interval Arithmetic in R |
| valuer | Pricing of Variable Annuities |
| VAR.etp | VAR modelling: estimation, testing, and prediction |
| varband | Variable Banding of Large Precision Matrices |
| varbvs | Large-Scale Bayesian Variable Selection Using Variational Methods |
| varComp | Variance Component Models |
| vardiag | Variogram Diagnostics |
| vardpoor | Variance Estimation for Sample Surveys by the Ultimate Cluster Method |
| VarED | Variance Estimation using Difference-Based Methods |
| VaRES | Computes value at risk and expected shortfall for over 100 parametric distributions |
| VarfromPDB | Disease-Gene-Variant Relations Mining from the Public Databases and Literature |
| varhandle | Functions for Robust Variable Handling |
| VariABEL | Testing of Genotypic Variance Heterogeneity to Detect Potentially Interacting SNP |
| variables | Variable Descriptions |
| VariableScreening | High-Dimensional Screening for Semiparametric Longitudinal Regression |
| varian | Variability Analysis in R |
| VarianceGamma | The Variance Gamma Distribution |
| VarReg | Semi-Parametric Variance Regression |
| vars | VAR Modelling |
| VARSEDIG | An Algorithm for Morphometric Characters Selection and Statistical Validation in Morphological Taxonomy |
| varSel | Sequential Forward Floating Selection using Jeffries-Matusita Distance |
| VarSelLCM | Variable Selection for Model-Based Clustering using the Integrated Complete-Data Likelihood of a Latent Class Model |
| varSelRF | Variable Selection using Random Forests |
| VARsignR | Sign Restrictions, Bayesian, Vector Autoregression Models |
| VarSwapPrice | Pricing a variance swap on an equity index |
| vbdm | Variational Bayes Discrete Mixture Model |
| VBLPCM | Variational Bayes Latent Position Cluster Model for Networks |
| VBmix | Variational Bayesian Mixture Models |
| vbsr | Variational Bayes Spike Regression Regularized Linear Models |
| VCA | Variance Component Analysis |
| vcd | Visualizing Categorical Data |
| vcdExtra | 'vcd' Extensions and Additions |
| vcfR | Manipulate and Visualize VCF Data |
| vcrpart | Tree-Based Varying Coefficient Regression for Generalized Linear and Ordinal Mixed Models |
| VDA | VDA |
| VDAP | Peptide Array Analysis Tools |
| vdg | Variance Dispersion Graphs and Fraction of Design Space Plots |
| Vdgraph | Variance dispersion graphs and Fraction of design space plots for response surface designs |
| VdgRsm | Plots of Scaled Prediction Variances for Response Surface Designs |
| vdiffr | Visual Regression Testing and Graphical Diffing |
| vdmR | Visual Data Mining Tools for R |
| vec2dtransf | 2D Cartesian Coordinate Transformation |
| vecsets | like base::sets tools but keeps duplicate elements |
| VecStatGraphs2D | Vector Analysis using Graphical and Analytical Methods in 2D |
| VecStatGraphs3D | Vector analysis using graphical and analytical methods in 3D |
| vegalite | Tools to Encode Visualizations with the 'Grammar of Graphics'-Like 'Vega-Lite' 'Spec' |
| vegan | Community Ecology Package |
| vegan3d | Static and Dynamic 3D Plots for the 'vegan' Package |
| vegclust | Fuzzy Clustering of Vegetation Data |
| vegdata | Access Vegetation Databases and Treat Taxonomy |
| vegetarian | Jost Diversity Measures for Community Data |
| vein | Vehicular Emissions Inventories |
| velociraptr | Fossil Analysis |
| velox | Fast Raster Manipulation and Extraction |
| vembedr | Functions to Embed Video in HTML |
| venn | Draw Venn Diagrams |
| VennDiagram | Generate High-Resolution Venn and Euler Plots |
| venneuler | Venn and Euler Diagrams |
| vennplot | Venn Diagrams in 2D and 3D |
| verification | Weather Forecast Verification Utilities |
| versions | Query and Install Specific Versions of Packages on CRAN |
| vertexenum | Vertex Enumeration of Polytopes |
| VertexSimilarity | Creates Vertex Similarity Matrix for an Undirected Graph |
| VertexSort | Network Hierarchical Structure and Randomization |
| vesselr | Gradient and Vesselness Tools for Arrays and NIfTI Images |
| vetools | Tools for Venezuelan Environmental Data |
| VetResearchLMM | Linear Mixed Models - An Introduction with Applications in Veterinary Research |
| vfcp | Computation of v Values for U and Copula C(U, v) |
| VGAM | Vector Generalized Linear and Additive Models |
| VGAMdata | Data Supporting the 'VGAM' Package |
| VHDClassification | Discrimination/Classification in very high dimension with linear and quadratic rules |
| vhica | Vertical and Horizontal Inheritance Consistence Analysis |
| VideoComparison | Video Comparison Tool |
| vietnamcode | Convert Vietnam Provincial Codes |
| VIF | VIF Regression: A Fast Regression Algorithm For Large Data |
| VIFCP | Detecting Change-Points via VIFCP Method |
| VIGoR | Variational Bayesian Inference for Genome-Wide Regression |
| VIM | Visualization and Imputation of Missing Values |
| VIMGUI | Visualization and Imputation of Missing Values - Graphical User Interface |
| VineCopula | Statistical Inference of Vine Copulas |
| vines | Multivariate Dependence Modeling with Vines |
| violinmplot | Combination of violin plot with mean and standard deviation |
| vioplot | Violin plot |
| viopoints | 1-D Scatter Plots with Jitter Using Kernel Density Estimates |
| vipor | Plot Categorical Data Using Quasirandom Noise and Density Estimates |
| viridis | Default Color Maps from 'matplotlib' |
| viridisLite | Default Color Maps from 'matplotlib' (Lite Version) |
| virtualspecies | Generation of Virtual Species Distributions |
| virustotal | R Client for the VirusTotal API |
| ViSiElse | A Visual Tool for Behaviour Analysis |
| visNetwork | Network Visualization using 'vis.js' Library |
| visreg | Visualization of Regression Models |
| vistime | Pretty Timeline Creation |
| visualFields | Statistical Methods for Visual Fields |
| visualize | Graph Probability Distributions with User Supplied Parameters and Statistics |
| VisuClust | Visualisation of Clusters in Multivariate Data |
| vita | Variable Importance Testing Approaches |
| vitality | Fitting Routines for the Vitality Family of Mortality Models |
| VizOR | Graphical Visualization Tools for Complex Observational Data with Focus on Health Sciences |
| vkR | Access to VK API via R |
| VLF | Frequency Matrix Approach for Assessing Very Low Frequency Variants in Sequence Records |
| VLMC | Variable Length Markov Chains ('VLMC') Models |
| vmsbase | GUI Tools to Process, Analyze and Plot Fisheries Data |
| VNM | Using V-algorithm and Newton-Raphson Method to Obtain Multiple-objective Optimal Design |
| vocaldia | Create and Manipulate Vocalisation Diagrams |
| vortexR | Post Vortex Simulation Analysis |
| vortexRdata | Example Data for R Package 'vortexR' |
| Voss | Generic Voss algorithm (random sequential additions) |
| vote | Election Vote Counting |
| vottrans | Voter Transition Analysis |
| vowels | Vowel Manipulation, Normalization, and Plotting |
| vows | Voxelwise Semiparametrics |
| voxel | Mass-Univariate Voxelwise Analysis of Medical Imaging Data |
| VoxR | Metrics extraction of trees from T-LiDAR data |
| vqtl | Genome Scans to Accommodate and Target Genetic and Non-Genetic Effects on Trait Variance |
| vrcp | Change Point Estimation for Regression with Varying Segments and Heteroscedastic Variances |
| vrmlgen | Generate 3D visualizations for data exploration on the web |
| VRPM | Visualizing Risk Prediction Models |
| vrtest | Variance Ratio tests and other tests for Martingale Difference Hypothesis |
| vscc | Variable selection for clustering and classification |
| VSE | Variant Set Enrichment |
| VSURF | Variable Selection Using Random Forests |
| VTrack | A Collection of Tools for the Analysis of Remote Acoustic Telemetry Data |
| vtreat | A Statistically Sound 'data.frame' Processor/Conditioner |
| vudc | Visualization of Univariate Data for Comparison |
| VWPre | Tools for Preprocessing Visual World Data |
| vwr | Useful functions for visual word recognition research |
| W2CWM2C | A Graphical Tool for Wavelet (Cross) Correlation and Wavelet Multiple (Cross) Correlation Analysis |
| W3CMarkupValidator | R Interface to W3C Markup Validation Services |
| WACS | Multivariate Weather-State Approach Conditionally Skew-Normal Generator |
| waffect | A package to simulate constrained phenotypes under a disease model H1 |
| waffle | Create Waffle Chart Visualizations in R |
| wahc | Autocorrelation and Heteroskedasticity Correction in Fixed Effect Panel Data Model |
| wakefield | Generate Random Data Sets |
| walkr | Random Walks in the Intersection of Hyperplanes and the N-Simplex |
| walkscoreAPI | Walk Score and Transit Score API |
| wallace | A Modular Platform for Reproducible Modeling of Species Niches and Distributions |
| wally | The Wally Calibration Plot for Risk Prediction Models |
| wand | Retrieve 'Magic' Attributes from Files and Directories |
| warbleR | Streamline Bioacoustic Analysis |
| WARN | Weaning Age Reconstruction with Nitrogen Isotope Analysis |
| warpMix | Mixed Effects Modeling with Warping for Functional Data Using B-Spline |
| wasim | Visualisation and analysis of output files of the hydrological model WASIM |
| water | Actual Evapotranspiration with Energy Balance Models |
| waterData | Retrieval, Analysis, and Anomaly Calculation of Daily Hydrologic Time Series Data |
| waterfall | Waterfall Charts |
| waterfalls | Create Waterfall Charts using 'ggplot2' Simply |
| WaterML | Fetch and Analyze Data from 'WaterML' and 'WaterOneFlow' Web Services |
| Watersheds | Spatial Watershed Aggregation and Spatial Drainage Network Analysis |
| Wats | Wrap Around Time Series Graphics |
| waveband | Computes credible intervals for Bayesian wavelet shrinkage |
| waved | Wavelet Deconvolution |
| WaveletComp | Computational Wavelet Analysis |
| WaveLetLongMemory | Estimating Long Memory Parameter using Wavelet |
| wavelets | A package of functions for computing wavelet filters, wavelet transforms and multiresolution analyses |
| wavemulcor | Wavelet routine for multiple correlation |
| waver | Calculate Fetch and Wave Energy |
| WaverR | Data Estimation using Weighted Averages of Multiple Regressions |
| waveslim | Basic wavelet routines for one-, two- and three-dimensional signal processing |
| wavethresh | Wavelets Statistics and Transforms |
| wBoot | Bootstrap Methods |
| wbs | Wild Binary Segmentation for Multiple Change-Point Detection |
| wbstats | Programmatic Access to Data and Statistics from the World Bank API |
| wbsts | Multiple Change-Point Detection for Nonstationary Time Series |
| wccsom | SOM Networks for Comparing Patterns with Peak Shifts |
| WCE | Weighted Cumulative Exposure Models |
| wCorr | Weighted Correlations |
| WCQ | Detection of QTL effects in a small mapping population |
| WDI | World Development Indicators (World Bank) |
| wdman | 'Webdriver'/'Selenium' Binary Manager |
| weatherData | Get Weather Data from the Web |
| weathermetrics | Functions to Convert Between Weather Metrics |
| weatherr | Tools for Handling and Scrapping Instant Weather Forecast Feeds |
| webchem | Chemical Information from the Web |
| WebGestaltR | The R Version of WebGestalt |
| webmockr | Stubbing and Setting Expectations on 'HTTP' Requests |
| webp | A New Format for Lossless and Lossy Image Compression |
| webreadr | Tools for Reading Formatted Access Log Files |
| webshot | Take Screenshots of Web Pages |
| webuse | Import Stata 'webuse' Datasets |
| webutils | Utility Functions for Developing Web Applications |
| webvis | Create graphics for the web from R |
| wec | Weighted Effect Coding |
| weco | Western Electric Company Rules (WECO) for Shewhart Control Chart |
| WEE | Weighted Estimated Equation (WEE) Approaches in Genetic Case-Control Studies |
| Weighted.Desc.Stat | Weighted Descriptive Statistics |
| WeightedCluster | Clustering of Weighted Data |
| WeightedPortTest | Weighted Portmanteau Tests for Time Series Goodness-of-fit |
| weightedScores | Weighted Scores Method for Regression Models with Dependent Data |
| weightr | Estimating Weight-Function Models for Publication Bias |
| weights | Weighting and Weighted Statistics |
| weightTAPSPACK | Weight TAPS Data |
| weirs | A Hydraulics Package to Compute Open-Channel Flow over Weirs |
| welchADF | Welch-James Statistic for Robust Hypothesis Testing under Heterocedasticity and Non-Normality |
| wellknown | Convert Between 'WKT' and 'GeoJSON' |
| wesanderson | A Wes Anderson Palette Generator |
| wfe | Weighted Linear Fixed Effects Regression Models for Causal Inference |
| wfg | Weighted Fast Greedy Algorithm |
| wfindr | Crossword, Scrabble and Anagram Solver |
| wgaim | Whole Genome Average Interval Mapping for QTL Detection using Mixed Models |
| WGCNA | Weighted Correlation Network Analysis |
| wgeesel | Weighted Generalized Estimating Equations and Model Selection |
| wgsea | Wilcoxon based gene set enrichment analysis |
| WhatIf | Evaluate Counterfactuals |
| whisker | {{mustache}} for R, logicless templating |
| WhiteStripe | White Matter Normalization for Magnetic Resonance Images using Whitestripe |
| WHO | R Client for the World Health Organization API |
| whoami | Username, Full Name, Email Address, 'GitHub' Username of the Current User |
| whoapi | A 'Whoapi' API Client |
| WhopGenome | High-Speed Processing of VCF, FASTA and Alignment Data |
| wicket | Utilities to Handle WKT Spatial Data |
| widals | Weighting by Inverse Distance with Adaptive Least Squares for Massive Space-Time Data |
| widenet | Penalized Regression with Polynomial Basis Expansions |
| widgetframe | 'Htmlwidgets' in Responsive 'iframes' |
| wikibooks | Functions and datasets of the german WikiBook "GNU R" |
| WikidataQueryServiceR | API Client Library for 'Wikidata Query Service' |
| WikidataR | API Client Library for 'Wikidata' |
| wikilake | Scrape Lakes Metadata Tables from Wikipedia |
| WikipediaR | R-Based Wikipedia Client |
| wikipediatrend | Public Subject Attention via Wikipedia Page View Statistics |
| WikipediR | A MediaWiki API Wrapper |
| WikiSocio | A MediaWiki API Wrapper |
| wikitaxa | Taxonomic Information from 'Wikipedia' |
| WilcoxCV | Wilcoxon-based variable selection in cross-validation |
| wildcard | Templates for Data Frames |
| wildlifeDI | Calculate Indices of Dynamic Interaction for Wildlife Telemetry Data |
| wildpoker | Best Hand Analysis for Poker Variants Including Wildcards |
| windex | windex: Analysing convergent evolution using the Wheatsheaf index |
| wingui | Advanced Windows Functions |
| wiod | World Input Output Database 1995-2011 |
| wiqid | Quick and Dirty Estimates for Wildlife Populations |
| WiSEBoot | Wild Scale-Enhanced Bootstrap |
| withr | Run Code 'With' Temporarily Modified Global State |
| wkb | Convert Between Spatial Objects and Well-Known Binary Geometry |
| wle | Weighted Likelihood Estimation |
| WLreg | Regression Analysis Based on Win Loss Endpoints |
| WMCapacity | GUI Implementing Bayesian Working Memory Models |
| WMDB | Discriminant Analysis Methods by Weight Mahalanobis Distance and bayes |
| Wmisc | Wamser Misc: Reading Files by Tokens, Stateful Computations, Utility Functions |
| wmlf | Wavelet Leaders in Multifractal Analysis |
| wmtsa | Wavelet Methods for Time Series Analysis |
| wnominate | Roll Call Analysis Software |
| woe | Computes Weight of Evidence and Information Values |
| woeBinning | Supervised Weight of Evidence Binning of Numeric Variables and Factors |
| word.alignment | Computing Word Alignment Using IBM Model 1 (and Symmetrization) for a Given Parallel Corpus and Its Evaluation |
| wordbankr | Accessing the Wordbank Database |
| wordcloud | Word Clouds |
| wordcloud2 | Create Word Cloud by htmlWidget |
| wordmatch | Matches words in one file with words in another file |
| wordnet | WordNet Interface |
| WordPools | Classical word pools used in studies of learning and memory |
| wordspace | Distributional Semantic Models in R |
| worldmet | Import Surface Meteorological Data from NOAA Integrated Surface Database (ISD) |
| worrms | World Register of Marine Species (WoRMS) Client |
| WPC | Weighted Predictiveness Curve |
| wPerm | Permutation Tests |
| WPKDE | Weighted Piecewise Kernel Density Estimation |
| wpp2008 | World Population Prospects 2008 |
| wpp2010 | World Population Prospects 2010 |
| wpp2012 | World Population Prospects 2012 |
| wpp2015 | World Population Prospects 2015 |
| wppExplorer | Explorer of World Population Prospects |
| wqs | Weighted Quantile Sum Regression |
| wrangle | A Systematic Data Wrangling Idiom |
| Wrapped | Computes Pdf, Cdf, Quantile, Random Numbers and Provides Estimation for 40 Univariate Wrapped Distributions |
| wrapr | Wrap R Functions for Debugging and Ease of Use |
| wrassp | Interface to the ASSP Library |
| WrightMap | IRT Item-Person Map with 'ConQuest' Integration |
| write.snns | Function for exporting data to SNNS pattern files |
| WriteXLS | Cross-Platform Perl Based R Function to Create Excel 2003 (XLS) and Excel 2007 (XLSX) Files |
| WRS2 | A Collection of Robust Statistical Methods |
| wrspathrow | Functions for working with Worldwide Reference System (WRS) |
| wrspathrowData | Data used by the wrspathrow package |
| wrswoR | Weighted Random Sampling without Replacement |
| wrswoR.benchmark | Benchmark and Correctness Data for Weighted Random Sampling Without Replacement |
| WRTDStidal | Weighted Regression for Water Quality Evaluation in Tidal Waters |
| wru | Who are You? Bayesian Prediction of Racial Category Using Surname and Geolocation |
| wskm | Weighted k-Means Clustering |
| wsrf | Weighted Subspace Random Forest for Classification |
| wSVM | Weighted SVM with boosting algorithm for improving accuracy |
| wtest | The W-Test on Genetic Interactions Testing |
| WufooR | R Wrapper for the 'Wufoo.com' - The Form Building Service |
| wux | Wegener Center Climate Uncertainty Explorer |
| WVPlots | Common Plots for Analysis |
| wvtool | Image Tools for Automated Wood Identification |
| WWGbook | Functions and datasets for WWGbook |
| WWR | Weighted Win Loss Statistics and their Variances |
| x.ent | eXtraction of ENTity |
| x12 | Interface to X12-ARIMA/X13-ARIMA-SEATS and Structure for Batch Processing of Seasonal Adjustment |
| x12GUI | X12 - Graphical User Interface |
| x13binary | Provide the 'x13ashtml' Seasonal Adjustment Binary |
| xaringan | Presentation Ninja |
| xbreed | Genomic Simulation of Purebred and Crossbred Populations |
| XBRL | Extraction of Business Financial Information from 'XBRL' Documents |
| xergm | Extensions of Exponential Random Graph Models |
| xergm.common | Common Infrastructure for Extensions of Exponential Random Graph Models |
| xgboost | Extreme Gradient Boosting |
| xgobi | Interface to the XGobi and XGvis programs for graphical data analysis |
| XGR | Exploring Genomic Relations for Enhanced Interpretation Through Enrichment, Similarity, Network and Annotation Analysis |
| xhmmScripts | XHMM R scripts |
| XHWE | X Chromosome Hardy-Weinberg Equilibrium |
| XiMpLe | A Simple XML Tree Parser and Generator |
| xkcd | Plotting ggplot2 Graphics in an XKCD Style |
| xkcdcolors | Color Names from the XKCD Color Survey |
| XLConnect | Excel Connector for R |
| XLConnectJars | JAR Dependencies for the XLConnect Package |
| xLLiM | High Dimensional Locally-Linear Mapping |
| xlsimple | 'XLConnect' Wrapper |
| xlsx | Read, write, format Excel 2007 and Excel 97/2000/XP/2003 files |
| xlsxjars | Package required POI jars for the xlsx package |
| xlutils3 | Extract Multiple Excel Files at Once |
| xmeta | A Toolbox for Multivariate Meta-Analysis |
| Xmisc | Xiaobei's miscellaneous classes and functions |
| XML | Tools for Parsing and Generating XML Within R and S-Plus |
| xml2 | Parse XML |
| XML2R | EasieR XML data collection |
| xmlparsedata | Parse Data of 'R' Code as an 'XML' Tree |
| XMRF | Markov Random Fields for High-Throughput Genetics Data |
| XNomial | Exact Goodness-of-Fit Test for Multinomial Data with Fixed Probabilities |
| xoi | Tools for Analyzing Crossover Interference |
| Xplortext | Statistical Analysis of Textual Data |
| xpose4 | Tools for Nonlinear Mixed-Effect Model Building and Diagnostics |
| XR | A Structure for Interfaces from R |
| XRJulia | Structured Interface to Julia |
| XRPython | Structured Interface to Python |
| XRSCC | Statistical Quality Control Simulation |
| xseq | Assessing Functional Impact on Gene Expression of Mutations in Cancer |
| xslt | XSLT 1.0 Transformations |
| xtable | Export Tables to LaTeX or HTML |
| xtal | Crystallization Toolset |
| xtermStyle | Terminal Text Formatting Using Escape Sequences |
| xtractomatic | Accessing Environmental Data from ERD's ERDDAP Server |
| xts | eXtensible Time Series |
| xVA | Calculates Credit Risk Valuation Adjustments |
| xxIRT | Practical Item Response Theory and Computer-Based Testing in R |
| xyloplot | A Method for Creating Xylophone-Like Frequency Density Plots |
| xyz | The 'xyz' Algorithm for Fast Interaction Search in High-Dimensional Data |
| yacca | Yet Another Canonical Correlation Analysis Package |
| yaImpute | Nearest Neighbor Observation Imputation and Evaluation Tools |
| yakmoR | A Simple Wrapper for the k-Means Library Yakmo |
| YaleToolkit | Data exploration tools from Yale University |
| yaml | Methods to Convert R Data to YAML and Back |
| yarrr | A Companion to the e-Book "YaRrr!: The Pirate's Guide to R" |
| ycinterextra | Yield curve or zero-coupon prices interpolation and extrapolation |
| yCrypticRNAs | Cryptic Transcription Analysis in Yeast |
| yesno | Ask a Custom Yes-No Question |
| yhat | Interpreting Regression Effects |
| yhatr | R Binder for the Yhat API |
| YieldCurve | Modelling and estimation of the yield curve |
| ykmeans | K-means using a target variable |
| yorkr | Analyze Cricket Performances Based on Data from Cricsheet |
| YplantQMC | Plant Architectural Analysis with Yplant and QuasiMC |
| YPmodel | The Short-Term and Long-Term Hazard Ratio Model for Survival Data |
| YuGene | A Simple Approach to Scale Gene Expression Data Derived from Different Platforms for Integrated Analyses |
| yuima | The YUIMA Project Package for SDEs |
| yuimaGUI | A Graphical User Interface for the 'yuima' Package |
| yummlyr | R Bindings for Yummly API |
| zCompositions | Imputation of Zeros and Nondetects in Compositional Data Sets |
| zeallot | Multiple and Unpacking Variable Assignment |
| ZeBook | ZeBook Working with dynamic models for agriculture and environment |
| zebu | Local Association Measures |
| Zelig | Everyone's Statistical Software |
| ZeligChoice | Zelig Choice Models |
| ZeligEI | Zelig Ecological Inference Models |
| zeligverse | Easily Install and Load Stable Zelig Packages |
| zendeskR | Zendesk API Wrapper |
| zenplots | Zigzag Expanded Navigation Plots |
| zetadiv | Functions to Compute Compositional Turnover Using Zeta Diversity |
| zfa | Zoom-Focus Algorithm |
| ZIBseq | Differential Abundance Analysis for Metagenomic Data via Zero-Inflated Beta Regression |
| zic | Bayesian Inference for Zero-Inflated Count Models |
| ZillowR | R Interface to Zillow Real Estate and Mortgage Data API |
| ZIM | Zero-Inflated Models for Count Time Series with Excess Zeros |
| zip | Cross-Platform 'zip' Compression |
| zipcode | U.S. ZIP Code database for geocoding |
| zipfR | Statistical models for word frequency distributions |
| ziphsmm | Zero-Inflated Poisson Hidden (Semi-)Markov Models |
| zoeppritz | Seismic Reflection and Scattering Coefficients |
| zoib | Bayesian Inference for Beta Regression and Zero-or-One Inflated Beta Regression |
| zoo | S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations) |
| zooaRch | Analytical Tools for Zooarchaeological Data |
| zooaRchGUI | Analytical Tools for Zooarchaeological Data |
| zoocat | 'zoo' Objects with Column Attributes |
| zooimage | Analysis of numerical zooplankton images |
| zoom | A spatial data visualization tool |
| zoon | Reproducible, Accessible & Shareable Species Distribution Modelling |
| ZRA | Dynamic Plots for Time Series Forecasting |
| ztable | Zebra-Striped Tables in LaTeX and HTML Formats |
| zTree | Functions to Import Data from 'z-Tree' into R |
| ztype | Run a Ztype Game Loaded with R Functions |
| zyp | Zhang + Yue-Pilon trends package |


I'm currrently a student at Epicodus, which is a rather unique progamming school. Here are some projects I have written or contributed to:
I started playing the trombone when I was nine and it shaped the rest of my life.
I have a band How Things Work, which is an indie rock band with:
instead of the usual guitar.
Playing the trombone in rock bands meant I had to build my own gear. If you play a guitar, and you need an amp or something, you go to Guitar Center. There is no Trombone Center. I built my own amps and equipment so I could go onstage and plug in and play through a stack like any self-respecting guitarist. The photo (right) is of a microphone I made, part of a preamp system for my band that gives a proper signal to the club sound board from the horns. I love building this stuff, almost as much as playing the horn.
Being a trombonist meant I had to pick up a lot of other skills to pay the rent.
I've spent the last twenty years working as a:
as well as recording location sound for film.
This emphasais on function, design, and construction is what led me to pursue programming. I am very interested in the underlying structure of software and how the machine actually works.


The book is a very nice work of fiction by the author J.K Rowling.
Its about a boy who is gifted with supernatural powers.


Banked turns
Turning in a circle requires a vehicle to have a centripetal acceleration inwards on the turn, and so there must be some centripetal force that produces this acceleration. For a vehicle driving on flat ground, this force must be produced by a sideways friction force on the tires. This introduces two problems:
- If the coefficient of friction is not high enough (say the road is wet or icy), then the friction force will be insufficient and the vehicle will slide off the road.
- Even if the friction force is high enough, because it acts at the bottom of the tires, it produces a net moment about the center of mass, which can cause the vehicle to roll over.
To avoid both of these problems, the road can be banked inwards, so that the outer edge of the road is higher than the inner edge. This is called superelevation and means that some of the centripetal force can be provided by the normal force with the road, reducing the friction force and minimizing the risk of slip or roll.
The figure below shows a bus driving around a sharp corner at high speed on a heavily banked road. To understand the dynamics of this vehicle and the design tradeoffs for cornering on banked turns, we need a model. We will start below with a simple point mass model, which will be enough to understand friction and sliding, and then move on to a 2D rigid-body body to understand roll behavior.
Setra S 411 HD coach from the TopClass 400 series on the Mercedes-Benz Untertürkheim test track. This 10.16 m long coach model is relatively compact and has short overhangs, allowing it to traverse the sharp banked corner at about 100 km/h. The vehicle is powered by an OM 501 LA V6-engine developing 300 kW (408 hp). Image source: Daimler Global Media Site (full-sized image).
- Video of a Dodge Challenger on the Untertürkheim track curve.
- Video of the bus shown in Figure #avb‑fc.
- Video taken from inside a car driving around the Untertürkheim track.
Track geometry
To understand the dynamics of the bus above, we first need a simple model of the geometry of the road. A satellite photo of the track is shown below. The bus was photographed while on the far-left-hand curve, which we can see is approximately a half-circle with radius about 50 m.
Satellite image of the Mercedes-Benz test track at Untertürkheim, on the Neckar River just outside of Stuttgart, Germany. The picture of the bus above was taken on the far-left-hand corner, which has a radius of curvature of about 50 m. Integrated with the test track is the excellent Mercedes-Benz Museum.
To make a simple model, let us consider a track with straight segments and half-circular ends, as shown below. If we the bus then we can see the for velocity, acceleration, and force as the bus drives around the track. We assume constant speed, so there is only an acceleration when the velocity changes direction around the corners. This model assumes semi-circular track ends, but this is not a good idea in practice.
The centripetal acceleration must be caused by an inwards force while going around the corners. To understand the magnitude and source of this force, we now consider a simple point mass model.
The modern automobile was invented in 1885 by Karl Benz in Mannheim, Germany, about 100 km from Stuttgart. The commercial success of the new invention was significantly aided by Benz' wife Bertha, who marketed the automobile by making the world's first road trip, inventing brake linings in the process. Driven by the post-WWI German economic crisis and hyperinflation, the Benz company later merged with Daimler to form Daimler-Benz, and began producing Mercedes-Benz cars in Untertürkheim, just outside of Stuttgart, where it is still headquartered. One of the company's engineers, Ferdinand Porsche, later founded his own company, which is also still based in Stuttgart.
Point mass model
Consider the bus as a single point mass on the track while going around the corner. The diagram below shows the bus front-on, so the inside of the track is towards the left. We start by assuming there is no bank on the road and that the bus is stationary. The shows that the gravity force \(W\) is balanced by the normal force \(N\) from the road. If we now increase the bank angle \(\theta\) below, then we see that a friction force \(F\) tangential to the road is needed to keep the bus from sliding. In practice, this force will be limited by \(F \le \mu N\), where \(\mu\) is the coefficient of friction. If we incline the track until it is nearly vertical, we see that there a huge friction force would be required but only a tiny normal force is available. As this would not be possible without gluing the tires to the road, this situation would result in the bus sliding downwards towards the center of the track.
Road bank angle: \(\theta = \) \(^\circ\)
Bus speed: \(v = \) \(\rm m/s\)
Now set the bank angle back to \(0^\circ\) and make sure the speed is zero and that the is visible. Now increase the speed of the bus to produce a centripetal acceleration. Newton's law implies that there must be a centripetal friction force producing this acceleration. As the speed increases, we will eventually reach a point when the friction force required is too large and the bus will slide outwards on the track.
To avoid sliding outwards, we can increase the bank on the road. This increases the normal force and decreases the friction force, making it less likely that sliding will occur. For any given speed, there is some angle that exactly cancels the friction force, so that all the force is normal force. This is a very safe driving situation, as no sliding is possible, even in very slippery road conditions. On the real track, the bank angle increases as the bus drives higher. This allows the driver to select the correct incline angle to minimize sliding risk. We can also show the forces in or as total vectors as we change the bank angle and bus speed.
While this simple point mass model allows us to understand how banking the road can help with the problem of sliding, it does not tell us anything about rolling of the vehicle, because point masses can't rotate. We thus need a rigid body model to investigate roll.
Higher speed and a smaller radius of curvature on a circular track allows higher bank angles, as in the photo below.
2D Rigid body model
We now use a single rigid body model for the bus and we will do everything in 2D. Below we see a front-on diagram of the bus, with the inside of the track to the left. If we turn on the , then we see that the weight of the bus is supported by two forces, one on each wheel. If we increase \(\theta\) then the weight is supported more by the down-slope wheel, and we also see sideways friction forces appear to stop the bus from sliding. If the slope becomes too steep, then the normal force on the up-slope wheel reverses direction, indicating that it is holding the wheel down onto the road. Physically this cannot happen, so at this point the bus would tip over. We needed this rigid-body model to understand this tipping behavior, as we couldn't model this with the point mass model. Depending on the value of the friction coefficient, the bus may either slide or roll first as \(\theta\) increases.
Road bank angle: \(\theta = \) \(^\circ\)
Bus speed: \(v = \) \(\rm m/s\)
If we now set the bank angle \(\theta\) back to zero, we can start to increase the speed \(v\). Now we see that the wheel on the inside of the track takes less of the weight, and the friction forces now stop the bus from sliding outwards on the track. If the bus goes too fast then it will roll outwards on the track (assuming the friction coefficient is high enough that it didn't slide first).
Having considered both a banked road with stationary bus, and a moving bus on a horizontal road, we can now combine them to see the advantage of having a banked road when the bus is cornering. Choose a bus velocity, and then change the bank angle \(\theta\), so that the friction force is reduced (helping to avoid sliding) and the normal forces on the wheels are more evenly balanced (helping to avoid rolling). For any given speed, we can choose a bank angle so that there is simultaneously no sideways friction force and also exactly balanced normal forces. This is the safest bank angle for this speed.
Design of roads, tracks, and rail
A straight road generally has the center somewhat higher than the edges to allow water to run off. This is called cross slope or camber. When it is desired to have a banked turn, then the outer edge of the road is raised to produce superelevation, with the outer edge rising above both the center and the inner edge. The bank angle is chosen based on the radius of curvature of the turn and the expected speed of cars going around the turn, while still allowing for the fact that cars might be moving slowly or even stopped. The angle should thus not be chosen to eliminate all friction forces when cars are traveling at maximum speed, as this would be dangerous if traffic had to stop on the road.
Velodromes are arenas with tracks designed for high-speed bicycle races, as shown below, with speeds up to 85 km/h. The bank angle on velodrome tracks is chosen to minimize sideways forces on the bicycles when they are traveling at near maximum speeds, so the angle chosen depends on the radius of curvature of the track corners. For example, the Blaine velodrome track pictured below is 250 m long and has a 43° bank angle on the corners and 15° banking on the straightaways.
Bicycle riders on a banked turn at the Blaine velodrome, part of the National Sports Center in Blaine, Minnesota. Image source: flikr user flyinfoto (CC BY 2.0) (full-sized image).
High-speed trains such as the French TGV operate at speeds of over 300 km/h and have run at up to 575 km/h. To accommodate cornering at such speeds, track bends are constructed with a large radius of curvature (at least 7 km for new tracks) and a bank angle of up to about 7° (180 mm maximum superelevation with Standard gauge of 1435 mm). For railways, banking the track is know as cant.
An alternative approach for cornering with trains is to leave the track relatively flat and to tilt the train as it travels around a corner. This allows high-speed trains to operate on regular tracks, while maintaining safety and comfort for the passengers. For example, the Queensland Rail Tilt Train operates at 180 km/h by tilting at up to 5° around corners. As we see from our rigid body analysis above, tilting the train will help with avoiding tipping over at high speeds, but will not help with reducing horizontal friction forces. Even with a tilting train, entering a curve at too high a speed will lead to disastrous results.
The Blaine velodrome in Minnesota was designed by the famous Schuermann velodrome architects and built from the extremely durable African Afzelia hardwood. Velodrome races are always counter-clockwise and use special bikes with fixed chains (no freewheeling, no gears) and no brakes. The corners are scarily steep:
High-speed trains such as the French TGV and German ICE require many sophisticated engineering techniques to enable travel at extreme speed. Very smooth rails are needed, so the track is formed by welding the segments together into one precisely-aligned continuous steel line. The steel rails conduct heat too well for conventional welding, so thermite welding is used to join the track segments.
At high speeds the train drivers are unable to see railway signals on the side of the track. Electronic signaling systems are used to communicate to the drivers, either through the tracks for the TGV or via additional cables for the ICE.
Friction-based brakes produce too much heat, as they would need to absorb the kinetic energy of several hundred tonnes of train moving at over 300 km/h. Instead, dynamic breaking is used, in which the electric motors powering the trains are run in reverse as generators and the energy is shed as heat in resistor arrays. At extremely high speeds, the wheels themselves cannot provide sufficient traction to enable braking, so technologies like magnetic induction brakes are being developed, which directly push against the rails without involving the wheels at all.
Bus model
The bus model used above is a Setra S 411 HD coach. The mass of the vehicle is 12.6 t and the dimensions are shown below.
|
\[\begin{aligned} h_1 &= {0.87\rm\ m} & \ell_1 &= {0.30\rm\ m} \\ h_2 &= {2.24\rm\ m} & \ell_2 &= {0.97\rm\ m} \\ & & \ell_3 &= {0.97\rm\ m} \\ & & \ell_4 &= {0.30\rm\ m} \end{aligned}\] |


{% extends 'bom/bom-base.html' %} {% load staticfiles %} {% load materializecss %} {% block head-title %}{{ title }}{% endblock %} {% block main %} {{ add_subpart_form.media }} {% endblock %} {% block bom-menu %} {% endblock %} {% block content %}
To add batch subparts, upload a csv that contains at least two columns with the headers: part_number or manufacturer_part_number and quantity; the 'part_number' or 'manufacturer_part_number' column must contain part numbers that have already been created in this system.
Additional fields include 'reference' or 'designator' to add a reference field to the subpart; 'do_not_load' if you don't want the part loaded into the assembly that contains it; 'dnp' if you don't want to upload the part to IndaBOM.


HSP : Hot Soup Processor ver3.6 / onion software 1997-2021(c)
Professional student material is a programming live broadcast character "Professional student" This material was created in collaboration with the HSP3 mascot character "Tamaon-chan" . Please enjoy the co-starring of Tamane-chan and professional students.
Tamane-chan and Prosei-chan 2D materials are in the "sample / pronama2d" and "hsptv" folders in the HSP installation folder.
It contains.
A png format file is available. The background is filled with a color (black) that is transparent with standard HSP. It also contains an alpha channel value that is transparent in HSP3Dish.
"Mini_kei.png" and "mini_tamane.png" are the character images of "Mini Mini Kei-chan" and "Mini Mini Tamane-chan".
A total of 15 types (+ character shadows) of fantasy-style battle poses (5 types of facial expressions x 2) drawn with 256 x 256 dots and poses of regular clothes (5 types of facial expressions) are lined up.
The "sample / pronama2d" folder also contains large images. 15 kinds of variations are prepared for each file for both Tamane-chan and Professional Student-chan.
"Mini_chr.hsp" is a sample using the image of 256 x 256 dots in the "HSPTV" folder.
Also, "big_chr.hsp" is a sample using a large image.
It can be used in various situations depending on the idea. Sample scripts for displaying on HSP3 are also available, so please use them in your own programs and games.
mini_kei.png mini_tamane.png kei_knight1_1.png kei_knight1_2.png kei_knight1_3.png kei_knight1_4.png kei_knight1_5.png kei_knight2_1.png kei_knight2_2.png kei_knight2_3.png kei_knight2_4.png kei_knight2_5.png kei_school1_1.png kei_school1_2.png kei_school1_3.png kei_school1_4.png kei_school1_5.png tamane_wizard1_1.png tamane_wizard1_2.png tamane_wizard1_3.png tamane_wizard1_4.png tamane_wizard1_5.png tamane_wizard2_1.png tamane_wizard2_2.png tamane_wizard2_3.png tamane_wizard2_4.png tamane_wizard2_5.png tamane_school1_1.png tamane_school1_2.png tamane_school1_3.png tamane_school1_4.png tamane_school1_5.png pronama.bmp
The copyrights of the materials included in "HSP3 Prosei-chan Edition" are owned by the following authors.
"Mini Mini Kei-chan" "Mini Mini Tamane-chan" "editor.png" Author: Shuya Twitter https://twitter.com/mobius6001
Kei Kurui (Professional student) MMD / Unity / FBX official data (Nikoni solid) Character design: Ixy 3D modeling: micco, silver lion
"Maid-style professional student (pronama.bmp)" Author: Y people Twitter https://twitter.com/y_jinn
The materials included in "HSP3 Pro Nama-chan Edition" can be freely used and distributed by users according to the guidelines.
(There is no obligation to write the copyright even if you use the data. Also, you can use it in any form, not limited to HSP3.)
When using the material, be sure to read the usage guidelines for "Professional Student" and "Tamane-chan".
Professional student (Kei Kurui) usage guidelines https://pronama.azurewebsites.net/pronama/guideline/ Tamane-chan usage guidelines https://hsp.tv/make/tamane2d.html


Watch: Dog rescued after falling through ice into frozen lake
A dog which fell through ice into a frozen lake had a lucky escape after being saved by rescuers.
In a video posted on Facebook, rescuers from the Winnipeg Fire Paramedic Service in Canada can be seen boating out into the middle of the lake through a layer of ice before taking the dog back to land.
As the dog bounded on to the snowy ground, one rescuer could be heard saying: “Wow, he’s doing great!”
The City of Winnipeg wrote: “Dog owners are reminded to keep their dogs on leash around frozen waterways to prevent them from chasing geese onto the ice – ice surfaces are thin and fragile, and falling into ice-cold water can be deadly.”


Dear Sir/Madam, This mail will definitely be coming to you as a surprise, but I must crave your indulgence to introduce myself to you. I am Mr. John mensah Sulikuh, a supervisory manager at the United Trust Bank Ltd,London. However I got your contact through the International web directory.Recently we discovered a Dormant Account with a huge Amount of Money ValuedUSD17, 600,000.00 (Seventeen Million, six hundred Dollars Only) that belongs to One of our late Customer MR. KRAANVOGEL DIONIJS. who died In a plane crash. During our investigation and auditing in this bank, my Department came Across a very huge sum of money belonging to MR. KRAANVOGEL DIONIJS from Beverwijk, Netherlands, who died along with his wife, MRS. KATRIEN DIONIJS and Daughter, MS. SOFIE DIONIJS so the fund has Been dormant in his account with this Bank without Any claim of the fund in our custody either from his Family or relation before Our discovery to this development. The Banking law here stipulates that if such money remains unclaimed for 15 years, it will be forfeited to the Bank Treasury as an unclaimed bill. It is only a foreigner that can stand as a next of kin and it is upon this discovery that I decided to contact you to collaborate with you to pull out this dormant fund.In order to avert this negative development. On behalf of my trusted colleagues we now seek your permission to have you stand in as next of kin to Our late Customer so that the fund will be released and paid into your account as the beneficiary’s next of kin now that the bank is still expecting a next of kin or relative of the deceased, MR. KRAANVOGEL DIONIJS. We could have done this deal alone but because of our position in this country as civil servants, we are not allowed to operate a foreign account and that would eventually raise an eye brow on our side during the time of transfer since we still work in this bank, this is the actual reason why we required a second party or fellow who will assist us forward claims as the next of kin and also provide either an existing bank account or to set up a new Bank a/c immediately to receive this fund, even an empty a/c can also serve for this purpose. However, on smooth conclusion of this transaction, you will be entitled to 25% as gratification for your assistance to us, while 5% will be set aside to take care of expenses on both sides that may arise during the time of transfer and also for telephone bills, and the remaining 70% will be for me and my colleague. What I want from you is for you to act as the deceased next of kin. I have in my possession, all the necessary Documents to successfully accomplish the operation. Bear in mind that this proposal is 100% risk free. Further Information will be given to you as soon as I receive your positive response. I suggest you get back to me as soon as possible stating your wish. For confidentiality please contact me on my personal email<EMAIL_ADDRESS>and<EMAIL_ADDRESS><EMAIL_ADDRESS>Best Regards, Mr. John Mensah Sulikuh United Trust Bank Ltd


GO BACK
: ES5, ES6, JavaScript engine, HTTP, HTTPS, evergreen, transcompilation, DOM,
document object model, DOM tree, nodes, child nodes, root node, document, html, head, body, Node
class, parentNode, childNodes, nodeName, nodeType, traverse, depth-first, pre-order traversal,
TreeWalker, createTreeWalker, direct parent, direct child, descendant, getElementById,
getElementsByClassName, getElementsByTagName, HTMLCollection, CSS selectors, querySelectorAll,
createElement, insertBefore, appendChild, data attributes, events, capturing, bubbling, AJAX,
asynchronous JavaScript and XML, cross-origin resouce sharing (CORS),
Definitions
- The is a tree with
- The is consisting of single child element
- The element consists of two nodes: and
- Every node in the DOM is an instance of the class, with properties
and
Data Attributes
- introduced , which allow you to add
arbitrary data to HTML elements:
data-action="highlight"
data-containing="unique"
Events
- The describes almost and each browser
further implements nonstandard events
- Every element has the method that specifies a function when
an event occurs:
a.addEventListener('click', evt => { evt.preventDefault(); ...
});
- That function takes one argument that specifies: ...
Default Handlers and "preventDefault"
- The is designed to allow multiple handlers to handle the same
event, with many events having , for instance
click requests a new page to be loaded
- To prevent this behavior, use:
evt.preventDefault()
Event Capturing and Bubbling
- Because HTML is , events can be handled in multiple places.
- One option is to start at the this is called
- The other option, is to start where the , and up the hierarchy so all ancestors have a chance to
respond.
- Each can do one of three things:
- Cancel the event with which sets the
property to true, but continues to propogate
- Call which stops propagation past the current
element (all handlers for the current element will be called)
- Call , will prevent all propagation,
including handlers on the current element
Event Categories
- Drag events:
dragstart, drag, dragend, dragstop, drop
- Focus events:
focus, blur, change
- Form events:
submit
- Input device events:
click, mousedown, move, mouseup, mouseenter,
mouseleave, mouseover, mousewheel, keydown, keypress, keyup
- Media events:
pause, play
- Progress events:
load, error
- Touch events:
touches, pinch, swipe
AJAX
- , an acronym for , allows
elements be refreshed from the server without reloading the entire page
- leverages and
technology introduced in the early 2000s
- We will create a simple in to expose an Ajax
(see ajaxServer.js)
Code


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by Nick
Posted on 23 November, 2016
For the second year in a row, the Reich Lab is participating in the CDC FluSight challenge, a project where teams from around the country submit real-time predictions of influenza to the CDC. The teams use a variety of different models and methods to generate these predictions, from an empirical Bayes method that uses Google search data to a extended Kalman-filter method that uses humidity data to our kernel conditional density estimation method using recent incidence, and there are many others!
This year, we – well, mostly Evan – have developed a new ensemble method that combines predictions from different models. We – mostly Abhinav – also created a visualizer for our predictions. Check it out here! It’s still early in the season, and we’re not seeing much data to suggest that this will be an unusually high or low year, but that’s largely because there just isn’t much information in the early-season data. In this post, I’m going to give you a quick tour under the hood of our ensemble forecasting methodology. At some point, we’ll have an article up on GitHub or arXiv, but for now, this explanation will have to suffice.
We call our team the Kernel of Truth (left over from our KCDE methods last year, although we hope the name still is appropriate). The contest is based on predicting a measure of influenza incidence that represents the percentage of all doctor’s visits that are for influenza-like-illness (ILI), weighted by population. The measure is called “weighted ILI” and its units are percentage points. Per contest rules, all submissions have to submit full predictive distributions each week from November through April for seven different targets of interest, for each of the HHS regions in the U.S. (and for the country as a whole). Here are the targets
For our submissions this year, we obtain the final predictive distributions as a weighted average of predictions from three component models:
The final predictions are obtained as a linear combination of the predictions from these component models using a method known as “stacking” or model averaging. The model weights depend on the week of the season in which the predictions are made. There is a lot of gnarly math and computation that we’re leaving out here, but if you’d like to see it let us know in the comments section and post some more details. We estimate the weights via gradient tree boosting, optimizing leave-one-season-out crossvalidated log scores (using the definition of log scores specified for this competition). Currently we are using the xgboost
package in R to implement this, although there have been some rumblings about moving to another method, as this one is giving us some problems when the curvature of our loss function is negative. I’ll spare you the details for now.
We are submitting two variations on the ensemble model:
There is a lot more work to do on this to get it to where we want to it be, but one of the “advantages” of these challenges is that they force you to get stuff out there and just try it out. Some of the things that we are thinking about doing are improving the estimation methodology for the weights (including perhaps some kind of smoothing or regularization of the weights), adding a more mechanistic model that incorporates some biological features of flu, and incorporating the uncertainty in recent observations (as you can see in the app, there can be adjustments to reported cases, especially in the most recently reported weeks). So, there’s lots to do, and we’re hopefully just getting started.


Anders Transhuman Page - subpages there also have several references
to me and my works.
Dr. Matrix sites of Science Excellence
World Future Society / Greater Boston chapter home page
Home page of Ron Hale-Evans
Derek Strong - a good friend of mine
Russell Whitaker's Interesting People
Amara's Digital Web
Jason's Freethinking Polymath pages
Seeker1's CyberAnthropology Page
Symposium on Theories and Metaphors of Cyberspace
Idea Futures
Bionomics page by
Brad Cox.
Dave Krieger's Exceptional people
Home page of Joe Edelman
BGSU Department of Telecommunications resources
New Riders' Official World Wide Web Yellow Pages
Christian Jensen's Russian List
Living Systems page
Virian Net Resources
BRAIN, MIND, AND BEHAVIOR


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If you are an employer. You would definitly be interested in this:
My Resume. Thank your viewing and
I'd appreciate it very much if you are willing to give me any advise or give me a job opportunity. Here is my journey of study.
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I am a little geek👩💻👩🔬,
being fascinated by New Tech!Programming and Psychology have occupied an important place in my life.
As for coding, I learn them via Projects. you can view some of my tutorials from:
My technology blog
I also consider the idea of being a neuroscientist👩🔬 is extremly cool,
and I may spend the rest of my life to pursue it!
Above 👆 are my notes for my neuroscience learing~
My working experience
I might be a "master" in studing study
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New Wallet
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Faucet
Send Spend
Staking
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GENERATE NEW WALLET
Keep mnemonic and private key on secure place (recomended offline)
they are the key for your wallet.
You will lose access to your funds if you lose it
We don't keep a copy for you
REGENERATE WALLET FROM MNEMONIC
Insert Mnemonic
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Public Key
Spend Address
Balance
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Query Balance
Spend Faucet
The rules are:
1 Givaway Per User
Blacklist Period of 24 Hours
Recieve 50 Spend and Stake test coins
Spend Address
Request Coins
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Delegate Tokens
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Brodcast
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spend
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indent
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--- layout: post title: Maintenance date: 2004-12-05 00:00:00 category: personal ---
Normal service will be restored as soon as possible... I'm about to start messing with the layout and template for this site...
Update: right, done. To mark a year since I started using MovableType, and because I've generally become frustrated with t'blog over the last few months, I've reworked the template.
Why? Lots of reasons. The main one is that I don't like having titles for my posts - I think MovableType's default behaviour of requiring them has changed the way I write here; I don't just pop thoughts and ideas up like I used to when I used Radio Userland, but rather there's a pressure to think of a summary (usually something involving a pun) every time I post. I can still put titles onto posts of course - and probably shall continue to, for the benefit of folks reading my RSS feed.
The other reasons - blogroll being of limited value to anyone, the old template being a bit mucky and too "default" for my liking - are all minor by comparison. I'm interested to see if I start writing less but more often now that I've swapped over.
Anyhow, if you notice any problems, or have difficulty reading any of the site, my apologies. Drop a comment here or email me and I'll fix it...


In Angular using the filter in views can be pretty straight forward when wanting to search for key terms, but what about when we want to do some thing a little more involved? What about Filter that does not equal data? In this post I am going to describe how you can build a custom filter that will let us dive a little more into the data.
Filtering Out Records
Basically to create a custom filter, we create a function that we pass data into and return true or false. To filter out records that don’t match a specific set of data we need to build a custom function that will evaluate our records and then pass that function into the filter.
Let’s goto our view-controller.js
and add our custom function that will evaluate the records passed into it.
Here we are binding our custom function to our $scope variable removeSelectedFriends
. This function takes a friend object and compares it’s id
against the selected friends and returns true or false. This allows us to prevent our user from selecting the same friend twice.
In our view we will have:
Since we bound our function to $scope.removeSelectedFriends
, we can now just pass in removeSelectedFriends to the filter and it will evaluate every recod in the friends collection with our custom function.
Adding multiple filters
I still want my users to be able to search their friends list, to add additional filters you just separate them with an additional pipe. My final code looks something like this:


Schools shut because of Delhi's smog, but hundreds of children run a race
NEW DELHI: About 1,000 children ran a race in thick smog in the Indian capital New Delhi early on Thursday (Nov 14) when authorities declared air quality as hazardous and the city government ordered schools to be shut as part of emergency measures.
Reuters partner ANI tweeted pictures of several children running without protective masks as part of the annual "Run for Children" organised by a not for profit group called "Prayas" that runs programmes for child protection, juvenile justice and education.
The annual run, near New Delhi's diplomatic enclave, was organised to mark the birth anniversary of India's first prime minister, Jawaharlal Nehru, which is celebrated as Children's Day.
"The children did not run a long distance. It was a short run," Prayas' general secretary, Amod K Kanth, told Reuters.
"They come voluntarily here ... We got permissions from the police, permission from the government, permission for this place," said Kanth, a former senior police officer. He said the organisers did not have any directions from authorities to cancel the event.
READ: New Delhi in 'day-to-day battle' with smog as cool weather sets in
Activists say despite choking levels of pollution, residents of Delhi largely ignore the health risks. Prime Minister Narendra Modi has not spoken about the crisis even though he frequently takes to Twitter to promote his priorities.
The air quality index on Thursday was at a staggering 472 on a scale of 500, indicating possible serious respiratory risks for the general public, according to SAFAR, a government pollution monitor.
Air quality has remained hazardous for three consecutive days, which prompted the Environment Pollution Prevention & Control Authority (EPCA), a body appointed by the Supreme Court, to order shutting down of schools late on Wednesday.
"Keeping in mind the hazardous exposure to children ... all schools must remain shut for the next two days," Bhure Lal, the chairman of EPCA, said in a letter to the city government of Delhi and to its neighbouring states.
READ: Key facts behind Indian capital's toxic smog
The city government has already restricted private cars until Nov 15 with an "odd-even" system, banning them on alternate days based on licence plate numbers.
Angry residents took to social media and blamed authorities for organising the run.
"Run for Children ... in severe pollution? Theatre of the absurd," said Shashank Jaitely, a Twitter user.
The city government had no immediate comment.


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Version 3.9 has been released with a few new additions and improvements. For a summary of new features, please refer to the latest release notes.
The most significant one is the addition of a grid removal tool which should help eliminate the interfering grid lines that prevent the autodetection algorithms to work very well for certain plots. This initial implementation of the grid removal tool works with any grid line or background colors. You can remove horizontal and/or vertical lines as needed. The current implementation does not work with skewed or rotated plots, but that can be added in the future if it is useful.
To switch to the grid removal mode, select the Remove Grid menu option under the Axes menu as shown below:
You should now see a set of controls appear on the right side as shown in the image below. You can now proceed to draw a box over the region where you are interested in removing the grid. This is similar to drawing a mask in the automatic data extraction mode.
You can now proceed to specifying the colors. By default the Background Mode option is selected with the color selection as white. This means that the algorithm will assume the background is white in color and the grid lines have a color that lies outside the numeric color distance value (the number next to the Pick button). As most plots have white background and dark grid lines, the default settings should work well. If you want, you can adjust these settings or switch to the foreground mode as needed. The Test button should help you decide if your settings are working well.
Next, select the directions you want the grid removal tool to work in by clicking on the appropriate checkboxes. The X% and Y% values are two adjustable parameters that you can change to tweak the results. Usually, you will want to increase these numbers for solid grid lines and decrease for dotted or dashed lines. The default values should work well in most cases.
For more details, refer to the updated user manual.
In addition to the grid removal tool, there is support for log-scale radial direction in polar plots. Also the cubic spline interpolation for the "X step with interpolation" algorithm has been reimplemented for better performance and stability. In the process, the dependence on numeric.js has been completely removed.


Heatmap.Rd
Constructor method for Heatmap class
Heatmap(matrix, col, name, na_col = "grey", color_space = "LAB", rect_gp = gpar(col = NA), border = NA, cell_fun = NULL, layer_fun = NULL, jitter = FALSE, row_title = character(0), row_title_side = c("left", "right"), row_title_gp = gpar(fontsize = 14), row_title_rot = switch(row_title_side[1], "left" = 90, "right" = 270), column_title = character(0), column_title_side = c("top", "bottom"), column_title_gp = gpar(fontsize = 14), column_title_rot = 0, cluster_rows = TRUE, cluster_row_slices = TRUE, clustering_distance_rows = "euclidean", clustering_method_rows = "complete", row_dend_side = c("left", "right"), row_dend_width = unit(10, "mm"), show_row_dend = TRUE, row_dend_reorder = is.logical(cluster_rows) || is.function(cluster_rows), row_dend_gp = gpar(), cluster_columns = TRUE, cluster_column_slices = TRUE, clustering_distance_columns = "euclidean", clustering_method_columns = "complete", column_dend_side = c("top", "bottom"), column_dend_height = unit(10, "mm"), show_column_dend = TRUE, column_dend_gp = gpar(), column_dend_reorder = is.logical(cluster_columns) || is.function(cluster_columns), row_order = NULL, column_order = NULL, row_labels = rownames(matrix), row_names_side = c("right", "left"), show_row_names = TRUE, row_names_max_width = unit(6, "cm"), row_names_gp = gpar(fontsize = 12), row_names_rot = 0, row_names_centered = FALSE, column_labels = colnames(matrix), column_names_side = c("bottom", "top"), show_column_names = TRUE, column_names_max_height = unit(6, "cm"), column_names_gp = gpar(fontsize = 12), column_names_rot = 90, column_names_centered = FALSE, top_annotation = NULL, bottom_annotation = NULL, left_annotation = NULL, right_annotation = NULL, km = 1, split = NULL, row_km = km, row_km_repeats = 1, row_split = split, column_km = 1, column_km_repeats = 1, column_split = NULL, gap = unit(1, "mm"), row_gap = unit(1, "mm"), column_gap = unit(1, "mm"), show_parent_dend_line = ht_opt$show_parent_dend_line, heatmap_width = unit(1, "npc"), width = NULL, heatmap_height = unit(1, "npc"), height = NULL, show_heatmap_legend = TRUE, heatmap_legend_param = list(title = name), use_raster = (nrow(matrix) > 2000 && ncol(matrix) > 1) || (ncol(matrix) > 2000 && nrow(matrix) > 1), raster_device = c("png", "jpeg", "tiff", "CairoPNG", "CairoJPEG", "CairoTIFF"), raster_quality = 2, raster_device_param = list(), raster_resize = FALSE, post_fun = NULL)
| matrix | A matrix. Either numeric or character. If it is a simple vector, it will be converted to a one-column matrix. |
|---|---|
| col | A vector of colors if the color mapping is discrete or a color mapping function if the matrix is continuous numbers (should be generated by |
| name | Name of the heatmap. By default the heatmap name is used as the title of the heatmap legend. |
| na_col | Color for |
| rect_gp | Graphic parameters for drawing rectangles (for heatmap body). The value should be specified by |
| color_space | The color space in which colors are interpolated. Only used if |
| border | Whether draw border. The value can be logical or a string of color. |
| cell_fun | Self-defined function to add graphics on each cell. Seven parameters will be passed into this function: |
| layer_fun | Similar as |
| jitter | Random shifts added to the matrix. The value can be logical or a single numeric value. It it is |
| row_title | Title on the row. |
| row_title_side | Will the title be put on the left or right of the heatmap? |
| row_title_gp | Graphic parameters for row title. |
| row_title_rot | Rotation of row title. Only 0, 90, 270 are allowed to set. |
| column_title | Title on the column. |
| column_title_side | Will the title be put on the top or bottom of the heatmap? |
| column_title_gp | Graphic parameters for column title. |
| column_title_rot | Rotation of column titles. Only 0, 90, 270 are allowed to set. |
| cluster_rows | If the value is a logical, it controls whether to make cluster on rows. The value can also be a |
| cluster_row_slices | If rows are split into slices, whether perform clustering on the slice means? |
| clustering_distance_rows | It can be a pre-defined character which is in ("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman", "kendall"). It can also be a function. If the function has one argument, the input argument should be a matrix and the returned value should be a |
| clustering_method_rows | Method to perform hierarchical clustering, pass to |
| row_dend_side | Should the row dendrogram be put on the left or right of the heatmap? |
| row_dend_width | Width of the row dendrogram, should be a |
| show_row_dend | Whether show row dendrogram? |
| row_dend_gp | Graphic parameters for the dendrogram segments. If users already provide a |
| row_dend_reorder | Apply reordering on row dendrograms. The value can be a logical value or a vector which contains weight which is used to reorder rows. The reordering is applied by |
| cluster_columns | Whether make cluster on columns? Same settings as |
| cluster_column_slices | If columns are split into slices, whether perform clustering on the slice means? |
| clustering_distance_columns | Same setting as |
| clustering_method_columns | Method to perform hierarchical clustering, pass to |
| column_dend_side | Should the column dendrogram be put on the top or bottom of the heatmap? |
| column_dend_height | height of the column cluster, should be a |
| show_column_dend | Whether show column dendrogram? |
| column_dend_gp | Graphic parameters for dendrogram segments. Same settings as |
| column_dend_reorder | Apply reordering on column dendrograms. Same settings as |
| row_order | Order of rows. Manually setting row order turns off clustering. |
| column_order | Order of column. |
| row_labels | Optional row labels which are put as row names in the heatmap. |
| row_names_side | Should the row names be put on the left or right of the heatmap? |
| show_row_names | Whether show row names. |
| row_names_max_width | Maximum width of row names viewport. |
| row_names_gp | Graphic parameters for row names. |
| row_names_rot | Rotation of row names. |
| row_names_centered | Should row names put centered? |
| column_labels | Optional column labels which are put as column names in the heatmap. |
| column_names_side | Should the column names be put on the top or bottom of the heatmap? |
| column_names_max_height | Maximum height of column names viewport. |
| show_column_names | Whether show column names. |
| column_names_gp | Graphic parameters for drawing text. |
| column_names_rot | Rotation of column names. |
| column_names_centered | Should column names put centered? |
| top_annotation | A |
| bottom_annotation | A |
| left_annotation | It should be specified by |
| right_annotation | it should be specified by |
| km | Apply k-means clustering on rows. If the value is larger than 1, the heatmap will be split by rows according to the k-means clustering. For each row slice, hierarchical clustering is still applied with parameters above. |
| split | A vector or a data frame by which the rows are split. But if |
| row_km | Same as |
| row_km_repeats | Number of k-means runs to get a consensus k-means clustering. Note if |
| row_split | Same as |
| column_km | K-means clustering on columns. |
| column_km_repeats | Number of k-means runs to get a consensus k-means clustering. Similar as |
| column_split | Split on columns. For heatmap splitting, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-split . |
| gap | Gap between row slices if the heatmap is split by rows. The value should be a |
| row_gap | Same as |
| column_gap | Gap between column slices. |
| show_parent_dend_line | When heatmap is split, whether to add a dashed line to mark parent dendrogram and children dendrograms? |
| width | Width of the heatmap body. |
| height | Height of the heatmap body. |
| heatmap_width | Width of the whole heatmap (including heatmap components) |
| heatmap_height | Height of the whole heatmap (including heatmap components). Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#size-of-the-heatmap . |
| show_heatmap_legend | Whether show heatmap legend? |
| heatmap_legend_param | A list contains parameters for the heatmap legends. See |
| use_raster | Whether render the heatmap body as a raster image. It helps to reduce file size when the matrix is huge. Note if |
| raster_device | Graphic device which is used to generate the raster image. |
| raster_quality | A value set to larger than 1 will improve the quality of the raster image. |
| raster_device_param | A list of further parameters for the selected graphic device. For raster image support, please check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-as-raster-image . |
| raster_resize | Whether resize the matrix to let the dimension of the matrix the same as the dimension of the raster image? |
| post_fun | A function which will be executed after the heatmap list is drawn. |
The initialization function only applies parameter checking and fill values to the slots with some validation.
Following methods can be applied to the Heatmap-class
object:
show,Heatmap-method
: draw a single heatmap with default parameters
draw,Heatmap-method
: draw a single heatmap.
+
or %v%
append heatmaps and annotations to a list of heatmaps.
The constructor function pretends to be a high-level graphic function because the show
method
of the Heatmap-class
object actually plots the graphics.
A Heatmap-class
object.
# There is no example NULL#> NULL


Draggable Events
Lunch
Go home
Do homework
Work on UI design
Sleep tight
Create Event
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Chuan-Cuisine culture [located in Chengdu,Guiyang]
- 2016-11-18 the famous peper in Chuan-Cuisine
- 2016-08-20 Chuan-Cuisine resource and spirit
- 2016-08-12 search for the old Chuan-Cuisine
- 2016-08-01 Chuan-Cuisine history and characteristic
Xiang-Cuisine culture [located in Wuhan and Changsha]
- 2012-05-08 Xiang-Cuisine custom
- 2012-05-08 Xiang-Cuisine three characheristic
- 2010-11-29 Xiang-Cuisine speciality
- 2010-11-29 Xiang-Cuisine spirit
- 2010-10-31 interpret Xiang-Cuisine
Hui-Cuisine culture [located in Hefei]
- 2011-05-08 the development of Hui-Cuisine
- 2011-05-08 emphasing on Hui-Cuisine culture
- 2007-06-14 Hui-Cuisine,Hui cook and Hui-business contributing to China
- 2007-06-14 the current situation of Hui-Cuisine
- 2007-06-14 the traditional of Hui-Cuisine
- 2007-06-14 Hui-Cuisine status and influence


Empty Desert [ax24], desert, in Mandoria, wilderness
Last updated Turn #81
Empty Desert [ax24], desert, in Mandoria, wilderness
------------------------------------------------------------------------
27: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
27: arrived from Ocean [ax23], owner:
27: Phantasm [n4b], Imperial Empire, marquess, with eight sailors
27: Jupiter [m8j], Imperial Empire, accompanied by:
27: Odin [y5z], Imperial Empire
27: Zeus [y7k], Imperial Empire, the High Priest of the Church
27: of Kireus [156]
27: Hera [g3f], Imperial Empire
27: Thor [r4g], Imperial Empire
27: Apollo [b5e], Imperial Empire
27: Helios [p6i], Imperial Empire
27: Eos [a2t], Imperial Empire
27: Aryon [t6i], Imperial Empire
27: Lanthara [r4v], Imperial Empire
27: Licia [i1l], Imperial Empire
27: Thrix [n3v], Imperial Empire
27: Amanda [s7m], Imperial Empire
27: Klaus [u5x], Imperial Empire
28: Pack of wolves [hd8a], wolves, number: 9, went east.
28: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
28: departed for Ocean [ay24].
28: Pack of wolves [db8a], wolves, number: 7, arrived from the east.
29: Pack of wolves [hx4p], wolves, number: 7, went north.
30: Pack of wolves [db8a], wolves, number: 7, went east.
30: Pack of wolves [ca6t], wolves, number: 11, arrived from the north.
Routes leaving Empty Desert:
North, desert, to Empty Desert [aw24], 8 days
East, desert, to Empty Desert [ax25], 8 days
South, to Ocean [ay24], Crystal Sea, 2 days
West, to Ocean [ax23], Crystal Sea, 2 days
Inner locations:
Oasis [ae5o], oasis, 1 day, owner:
Giant lizard [ft2u], giant lizards, number: 31
Seen here:
Wild men [jl5a], savages, number: 38
New noble [d8r], refugee
Stack of bandits [ep3e], bandits, number: 65
Pack of wolves [hd8a], wolves, number: 9
Pack of wolves [hx4p], wolves, number: 7
Pack of wolves [db8a], wolves, number: 7
Pack of wolves [ca6t], wolves, number: 11


It's time for a party!
The xAPI Party is a gathering of tinkerers, explorers, strategic thinkers and people getting stuff done with the Experience API. It’s a day filled with case studies, how-to sessions, demos and networking. (OK, yes, it’s really a one-day conference, but Party sounds like – and is – a lot more fun.)
As you may know, xAPI is the up-and-coming spec for tracking learning and performance. While it’s rewarding to be at the forefront of xAPI’s implementation, there’s still so much to learn and so much yet to be decided by the industry. We find meeting with other curious trailblazers helps all involved. That’s why we host the 12-week learning-by-doing-in-teams-on-projects xAPI Learning Cohort every spring and fall. Folks from a diverse group of organizations, with a range of xAPI experience, form themselves into teams to explore a variety of use cases. And then we celebrate all that work with a Party.
We had sessions for the novice and the expert, for the tech-proficient and the tech-reluctant, and sessions for the Learning Cohort teams to share their projects. We simulcast a third of the sessions, and post the recordings.
You can attend the xAPI Party live or virtually, and registration is free. You do not need to have participated in the Cohort to join the Party, and vice versa.