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Package Name Access Summary Updated
r-fnn public Fast Nearest Neighbor Search Algorithms and Applications 2023-06-16
r-date public Functions for Handling Dates 2023-06-16
r-dfoptim public Derivative-Free Optimization 2023-06-16
r-geepack public Generalized Estimating Equation Package 2023-06-16
r-hexview public Viewing Binary Files 2023-06-16
r-mondate public Keep track of dates in terms of months 2023-06-16
r-goftest public Classical Goodness-of-Fit Tests for Univariate Distributions 2023-06-16
r-ibdreg public Regression Methods for IBD Linkage With Covariates 2023-06-16
r-prettyunits public Pretty, Human Readable Formatting of Quantities 2023-06-16
r-rem public Relational Event Models (REM) 2023-06-16
r-desc public Manipulate DESCRIPTION Files 2023-06-16
r-r.cache public Fast and Light-Weight Caching (Memoization) of Objects and Results to Speed Up Computations 2023-06-16
r-rcsdp public R Interface to the CSDP Semidefinite Programming Library 2023-06-16
r-cliapp public Create Rich Command Line Applications 2023-06-16
r-rex public Friendly Regular Expressions 2023-06-16
r-ini public Read and Write '.ini' Files 2023-06-16
r-downloader public Download Files over HTTP and HTTPS 2023-06-16
r-gamlss.data public GAMLSS Data 2023-06-16
r-png public Read and write PNG images 2023-06-16
r-visnetwork public Network Visualization using 'vis.js' Library 2023-06-16
r-base public R is a free software environment for statistical computing and graphics. 2023-06-16
r-rjsonio public This is a package that allows conversion to and from data in Javascript object notation (JSON) format. This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code and allows R programmers to read and convert JSON content to R objects. This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON and was not extensible. rjson's performance is now similar to this package, and perhaps slightly faster in some cases. This package uses methods and is readily extensible by defining methods for different classes, vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional options to allow customizing the generation and processing of JSON content. This package uses libjson rather than implementing yet another JSON parser. The aim is to support other general projects by building on their work, providing feedback and benefit from their ongoing development. 2023-06-16
aniso8601 public A library for parsing ISO 8601 strings. 2023-06-16
retrying public Simplify the task of adding retry behavior to just about anything. 2023-06-16
pywavelets public Discrete Wavelet Transforms in Python 2023-06-16
imageio public A Python library for reading and writing image data 2023-06-16
kiwisolver public A fast implementation of the Cassowary constraint solver 2023-06-16
r-foreign public Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ... 2023-06-16
r-squarem public Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent acceleration scheme. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("SQUAREM"). 2023-06-16
r-car public Functions and Datasets to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Second Edition, Sage, 2011 2023-06-16
r-quantmod public No Summary 2023-06-16
r-openxlsx public Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect' packages with the added benefit of removing the dependency on Java. 2023-06-16
r-pls public Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS). 2023-06-16
r-quantreg public Quantile Regression 2023-06-16
docker-pycreds public Python bindings for the docker credentials store API 2023-06-16
pyaml public PyYAML-based module to produce pretty and readable YAML-serialized data 2023-06-16
r-deoptimr public Differential Evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it only provides an implementation of the 'jDE' algorithm by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>. 2023-06-16
r-ddalpha public Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. 2023-06-16
r-numderiv public Methods for calculating (usually) accurate numerical first and second order derivatives. Accurate calculations are done using 'Richardson''s' extrapolation or, when applicable, a complex step derivative is available. A simple difference method is also provided. Simple difference is (usually) less accurate but is much quicker than 'Richardson''s' extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions. 2023-06-16
r-ttr public Technical Trading Rules 2023-06-16
r-lava public Latent Variable Models 2023-06-16
r-magic public Create and Investigate Magic Squares 2023-06-16
r-cvst public The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts. 2023-06-16
pycryptodome public Cryptographic library for Python 2023-06-16
r-ipred public Improved Predictors 2023-06-16
r-xts public eXtensible Time Series 2023-06-16
python-jose public JOSE implementation in Python 2023-06-16
jsondiff public Diff JSON and JSON-like structures in Python 2023-06-16
r-maptools public Tools for Handling Spatial Objects 2023-06-16
r-rcpproll public Provides fast and efficient routines for common rolling / windowed operations. Routines for the efficient computation of windowed mean, median, sum, product, minimum, maximum, standard deviation and variance are provided. 2023-06-16

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