# Thomas Lumley

#### 31 packages on CRAN

Converts between the Date class and d/m/y for several calendars, including Persian, Islamic, and Hebrew

Collapse red-green or green-blue distinctions to simulate the effects of different types of color-blindness.

Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.

The deterministic part of the Fortuna cryptographic pseudorandom number generator, described by Schneier & Ferguson "Practical Cryptography"

Functions for simple fixed and random effects meta-analysis for two-sample comparisons and cumulative meta-analyses. Draws standard summary plots, funnel plots, and computes summaries and tests for association and heterogeneity.

Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.

The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.

Two nonparametric methods for multiple regression transform selection are provided. The first, Alternative Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R.. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics.

Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R.

Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using 'opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R 'survival' package. (That is, the underlying Cox model code is derived from that in the R 'survival' package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future. Web applications are provided (via 'shiny') for the implemented methods to help in designing and deploying the computations.

Data from the six Australian Federal Elections (House of Representatives) between 2001 and 2016, and from the four Australian Censuses over the same period. Includes tools for visualizing and analysing the data, as well as imputing Census data for years in which a Census does not occur. This package incorporates data that is copyright Commonwealth of Australia (Australian Electoral Commission) 2016.

A forest plot that allows for multiple confidence intervals per row, custom fonts for each text element, custom confidence intervals, text mixed with expressions, and more. The aim is to extend the use of forest plots beyond meta-analyses. This is a more general version of the original 'rmeta' package's forestplot() function and relies heavily on the 'grid' package.

Various R programming tools for data manipulation, including: - medical unit conversions ('ConvertMedUnits', 'MedUnits'), - combining objects ('bindData', 'cbindX', 'combine', 'interleave'), - character vector operations ('centerText', 'startsWith', 'trim'), - factor manipulation ('levels', 'reorder.factor', 'mapLevels'), - obtaining information about R objects ('object.size', 'elem', 'env', 'humanReadable', 'is.what', 'll', 'keep', 'ls.funs', 'Args','nPairs', 'nobs'), - manipulating MS-Excel formatted files ('read.xls', 'installXLSXsupport', 'sheetCount', 'xlsFormats'), - generating fixed-width format files ('write.fwf'), - extricating components of date & time objects ('getYear', 'getMonth', 'getDay', 'getHour', 'getMin', 'getSec'), - operations on columns of data frames ('matchcols', 'rename.vars'), - matrix operations ('unmatrix', 'upperTriangle', 'lowerTriangle'), - operations on vectors ('case', 'unknownToNA', 'duplicated2', 'trimSum'), - operations on data frames ('frameApply', 'wideByFactor'), - value of last evaluated expression ('ans'), and - wrapper for 'sample' that ensures consistent behavior for both scalar and vector arguments ('resample').

Various R programming tools for plotting data, including: - calculating and plotting locally smoothed summary function as ('bandplot', 'wapply'), - enhanced versions of standard plots ('barplot2', 'boxplot2', 'heatmap.2', 'smartlegend'), - manipulating colors ('col2hex', 'colorpanel', 'redgreen', 'greenred', 'bluered', 'redblue', 'rich.colors'), - calculating and plotting two-dimensional data summaries ('ci2d', 'hist2d'), - enhanced regression diagnostic plots ('lmplot2', 'residplot'), - formula-enabled interface to 'stats::lowess' function ('lowess'), - displaying textual data in plots ('textplot', 'sinkplot'), - plotting a matrix where each cell contains a dot whose size reflects the relative magnitude of the elements ('balloonplot'), - plotting "Venn" diagrams ('venn'), - displaying Open-Office style plots ('ooplot'), - plotting multiple data on same region, with separate axes ('overplot'), - plotting means and confidence intervals ('plotCI', 'plotmeans'), - spacing points in an x-y plot so they don't overlap ('space').

Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP\_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes.

'haplo.ccs' estimates haplotype and covariate relative risks in case-control data by weighted logistic regression. Diplotype probabilities, which are estimated by EM computation with progressive insertion of loci, are utilized as weights.

To conduct Bayesian inference regression for responses with multilevel explanatory variables and missing values(Zeng ISL (2017) <doi:10.1101/153049>). Functions utilizing 'Stan', a software to implement posterior sampling using Hamiltonian MC and its variation Non-U-Turn algorithms are generated and provided to implement the posterior sampling of regression coefficients from the multilevel regression models. The package has two main functions to handle not-missing-at-random missing responses and left-censored with not-missing-at random responses. The purpose is to provide a similar format as the other R regression functions but using 'Stan' models.

Allows to pull data from MonetDB into R. Includes a DBI implementation and a dplyr backend.

An in-process version of 'MonetDB', a SQL database designed for analytical tasks. Similar to 'SQLite', the database runs entirely inside the 'R' shell.

Model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data.

Computes necessary information to meta analyze region-based tests for rare genetic variants (e.g. SKAT, T1) in individual studies, and performs meta analysis.

Computes necessary information to meta analyze SKAT statistics in each individual cohort, and then performs the meta analysis.

Use piping, verbs like 'group_by' and 'summarize', and other 'dplyr' inspired syntactic style when calculating summary statistics on survey data using functions from the 'survey' package.

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

A set of tools designed to facilitate easy adoption of R for students in introductory classes with little programming experience. Compiles output from existing routines together in an intuitive format, and adds functionality to existing functions. For instance, the regression function can perform linear models, generalized linear models, Cox models, or generalized estimating equations. The user can also specify multiple-partial F-tests to print out with the model coefficients. We also give many routines for descriptive statistics and plotting.