Package Name | Access | Summary | Updated |
---|---|---|---|

r-cghflasso | public | Spatial smoothing and hot spot detection using the fused lasso regression | 2019-08-21 |

r-blockmodeling | public | This is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>, Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>. | 2019-08-21 |

r-circular | public | Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific. | 2019-08-21 |

r-bglr | public | Bayesian Generalized Linear Regression. | 2019-08-21 |

r-coxphf | public | Implements Firth's penalized maximum likelihood bias reduction method for Cox regression which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function). The program fits profile penalized likelihood confidence intervals which were proved to outperform Wald confidence intervals. | 2019-08-21 |

r-gee | public | Generalized Estimation Equation solver. | 2019-08-21 |

r-akima | public | Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids. | 2019-08-21 |

r-hiddenmarkov | public | Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini. | 2019-08-21 |

r-clustercrit | public | Compute clustering validation indices. | 2019-08-21 |

r-coxboost | public | This package provides routines for fitting Cox models by likelihood based boosting for a single endpoint or in presence of competing risks | 2019-08-21 |

r-dppackage | public | Functions to perform inference via simulation from the posterior distributions for Bayesian nonparametric and semiparametric models. Although the name of the package was motivated by the Dirichlet Process prior, the package considers and will consider other priors on functional spaces. So far, DPpackage includes models considering Dirichlet Processes, Dependent Dirichlet Processes, Dependent Poisson- Dirichlet Processes, Hierarchical Dirichlet Processes, Polya Trees, Linear Dependent Tailfree Processes, Mixtures of Triangular distributions, Random Bernstein polynomials priors and Dependent Bernstein Polynomials. The package also includes models considering Penalized B-Splines. Includes semiparametric models for marginal and conditional density estimation, ROC curve analysis, interval censored data, binary regression models, generalized linear mixed models, IRT type models, and generalized additive models. Also contains functions to compute Pseudo-Bayes factors for model comparison, and to elicitate the precision parameter of the Dirichlet Process. To maximize computational efficiency, the actual sampling for each model is done in compiled FORTRAN. The functions return objects which can be subsequently analyzed with functions provided in the 'coda' package. | 2019-08-21 |

r-gam | public | Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990). | 2019-08-21 |

r-glasso | public | Estimation of a sparse inverse covariance matrix using a lasso (L1) penalty. Facilities are provided for estimates along a path of values for the regularization parameter. | 2019-08-21 |

libitk | public | Runtime libraries and header files for the Insight Toolkit for segmentation and registration. | 2019-08-21 |

graphql-relay | public | Relay implementation for Python | 2019-08-20 |

r-hier.part | public | Variance partition of a multivariate data set | 2019-08-20 |

xlsxwriter | public | A Python module for creating Excel XLSX files | 2019-08-20 |

r-lokern | public | Kernel regression smoothing with adaptive local or global plug-in bandwidth selection. | 2019-08-20 |

r-labdsv | public | A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses. | 2019-08-20 |

r-logicreg | public | Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) <DOI:10.1002/gepi.20042>. | 2019-08-20 |

r-maptpx | public | Posterior maximization for topic models (LDA) in text analysis, as described in Taddy (2012) `on estimation and selection for topic models'. Previous versions of this code were included as part of the textir package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling. | 2019-08-20 |

awscli | public | Universal Command Line Environment for AWS. | 2019-08-20 |

boto3 | public | Amazon Web Services SDK for Python | 2019-08-20 |

r-mpmi | public | Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. Uses a nonparametric bias correction giving Bias Corrected Mutual Information (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to large genomic datasets. | 2019-08-20 |

r-msgps | public | This package computes the degrees of freedom of the lasso, elastic net, generalized elastic net and adaptive lasso based on the generalized path seeking algorithm. The optimal model can be selected by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC. | 2019-08-20 |

r-multitaper | public | Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates. | 2019-08-20 |

r-norm | public | Analysis of multivariate normal datasets with missing values | 2019-08-20 |

marshmallow | public | A lightweight library for converting complex datatypes to and from native Python datatypes. | 2019-08-20 |

botocore | public | Low-level, data-driven core of boto 3. | 2019-08-20 |

hdmedians | public | High-dimensional medians | 2019-08-20 |

r-qtlrel | public | This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances. | 2019-08-20 |

r-pamr | public | Some functions for sample classification in microarrays. | 2019-08-20 |

r-signal | public | A set of signal processing functions originally written for 'Matlab' and 'Octave'. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions. | 2019-08-20 |

r-pmcmrplus | public | For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons tests (Tukey-Kramer test, Scheffe test, LSD-test) and many-to-one tests (Dunnett test) for normally distributed residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane's T2 test, Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals and heterogeneous variances are provided. Van der Waerden's normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks. Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included. In addition, a Friedman-test for one-way ANOVA with repeated measures on ranks (CRBD) and Skillings-Mack test for unbalanced CRBD is provided with consequent all-pairs tests (Nemenyi test, Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test). A trend can be tested with Pages's test. Durbin's test for a two-way balanced incomplete block design (BIBD) is given in this package as well as Gore's test for CRBD with multiple observations per cell is given. Outlier tests, Mandel's k- and h statistic as well as functions for Type I error and Power analysis as well as generic summary, print and plot methods are provided. | 2019-08-20 |

tesseract | public | An optical character recognition (OCR) engine | 2019-08-20 |

xgboost | public | Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow | 2019-08-20 |

py-xgboost-cpu | public | Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow | 2019-08-20 |

py-xgboost | public | Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow | 2019-08-20 |

r-xgboost-cpu | public | 2019-08-20 | |

r-xgboost | public | 2019-08-20 | |

libxgboost | public | 2019-08-20 | |

dbf | public | Pure python package for reading/writing dBase, FoxPro, and Visual FoxPro .dbf files (including memos) | 2019-08-20 |

r-subselect | public | A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. | 2019-08-20 |

r-softimpute | public | Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components) | 2019-08-20 |

r-subplex | public | The subplex algorithm for unconstrained optimization, developed by Tom Rowan <http://www.netlib.org/opt/subplex.tgz>. | 2019-08-20 |

r-timsac | public | Functions for statistical analysis, prediction and control of time series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>. | 2019-08-20 |

r-tmvtnorm | public | Random number generation for the truncated multivariate normal and Student t distribution. Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case. | 2019-08-20 |

r-tree | public | Classification and regression trees. | 2019-08-20 |

jupyterlab_server | public | A set of server components for JupyterLab and JupyterLab like applications. | 2019-08-20 |

r-bootstrap | public | Software (bootstrap, cross-validation, jackknife) and data for the book "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993, Chapman and Hall. This package is primarily provided for projects already based on it, and for support of the book. New projects should preferentially use the recommended package "boot". | 2019-08-20 |

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