Showing 21 of total 21 results (show query)
bioc
IRanges:Foundation of integer range manipulation in Bioconductor
Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames.
Maintained by Hervé Pagès. Last updated 2 months ago.
infrastructuredatarepresentationbioconductor-packagecore-package
22 stars 16.09 score 2.1k scripts 1.8k dependentsbioc
S4Vectors:Foundation of vector-like and list-like containers in Bioconductor
The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).
Maintained by Hervé Pagès. Last updated 2 months ago.
infrastructuredatarepresentationbioconductor-packagecore-package
18 stars 16.05 score 1.0k scripts 1.9k dependents
projectmosaic
mosaic:Project MOSAIC Statistics and Mathematics Teaching Utilities
Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.
Maintained by Randall Pruim. Last updated 1 years ago.
93 stars 13.32 score 7.2k scripts 7 dependents
plangfelder
WGCNA:Weighted Correlation Network Analysis
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
Maintained by Peter Langfelder. Last updated 6 months ago.
54 stars 9.65 score 5.3k scripts 32 dependents
tomasfryda
h2o:R Interface for the 'H2O' Scalable Machine Learning Platform
R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Maintained by Tomas Fryda. Last updated 1 years ago.
3 stars 8.20 score 7.8k scripts 11 dependentsbioc
BumpyMatrix:Bumpy Matrix of Non-Scalar Objects
Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects.
Maintained by Aaron Lun. Last updated 3 months ago.
softwareinfrastructuredatarepresentation
1 stars 6.62 score 39 scripts 12 dependents
cran
compositions:Compositional Data Analysis
Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
Maintained by K. Gerald van den Boogaart. Last updated 1 years ago.
1 stars 6.35 score 36 dependents
mikejareds
hermiter:Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)
Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) <doi:10.1016/j.jmva.2021.104783>.
Maintained by Michael Stephanou. Last updated 7 months ago.
cumulative-distribution-functionkendall-correlation-coefficientonline-algorithmsprobability-density-functionquantilespearman-correlation-coefficientstatisticsstreaming-algorithmsstreaming-datacpp
15 stars 5.11 score 17 scripts
spkaluzny
splusTimeDate:Times and Dates from 'S-PLUS'
A collection of classes and methods for working with times and dates. The code was originally available in 'S-PLUS'.
Maintained by Stephen Kaluzny. Last updated 2 months ago.
4.94 score 58 scripts 2 dependents
edsandorf
spdesign:Designing Stated Preference Experiments
Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).
Maintained by Erlend Dancke Sandorf. Last updated 5 months ago.
4.60 score 20 scriptscran
NADA:Nondetects and Data Analysis for Environmental Data
Contains methods described by Dennis Helsel in his book "Nondetects And Data Analysis: Statistics for Censored Environmental Data".
Maintained by Lopaka Lee. Last updated 5 years ago.
2 stars 4.45 score 14 dependentsbioc
zitools:Analysis of zero-inflated count data
zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging.
Maintained by Carlotta Meyring. Last updated 5 months ago.
softwarestatisticalmethodmicrobiome
4.40 score 6 scripts
kiangkiangkiang
ggESDA:Exploratory Symbolic Data Analysis with 'ggplot2'
Implements an extension of 'ggplot2' and visualizes the symbolic data with multiple plot which can be adjusted by more general and flexible input arguments. It also provides a function to transform the classical data to symbolic data by both clustering algorithm and customized method.
Maintained by Bo-Syue Jiang. Last updated 2 years ago.
21 stars 4.02 score 9 scripts
spkaluzny
splusTimeSeries:Time Series from 'S-PLUS'
A collection of classes and methods for working with indexed rectangular data. The index values can be calendar (timeSeries class) or numeric (signalSeries class). Methods are included for aggregation, alignment, merging, and summaries. The code was originally available in 'S-PLUS'.
Maintained by Stephen Kaluzny. Last updated 6 months ago.
3.95 score 20 scripts 1 dependents
wrathematics
kazaam:Tools for Tall Distributed Matrices
Many data science problems reduce to operations on very tall, skinny matrices. However, sometimes these matrices can be so tall that they are difficult to work with, or do not even fit into main memory. One strategy to deal with such objects is to distribute their rows across several processors. To this end, we offer an 'S4' class for tall, skinny, distributed matrices, called the 'shaq'. We also provide many useful numerical methods and statistics operations for operating on these distributed objects. The naming is a bit "tongue-in-cheek", with the class a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he starred in the film 'Kazaam'.
Maintained by Drew Schmidt. Last updated 8 years ago.
3.82 score 133 scriptscran
RSDA:R to Symbolic Data Analysis
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Maintained by Oldemar Rodriguez. Last updated 1 years ago.
1 stars 3.26 score 3 dependentscran
ibmdbR:IBM in-Database Analytics for R
Functionality required to efficiently use R with IBM(R) Db2(R) Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in conjunction with IBM Db2 Analytics Accelerator for z/OS. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the 'SparkR' package (<https://spark.apache.org/docs/latest/sparkr.html>). The optional 'ggplot2' package is needed for the plot.idaLm() function only.
Maintained by Shaikh Quader. Last updated 1 years ago.
2 stars 3.00 score
quantumofmoose
complexlm:Linear Fitting for Complex Valued Data
Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'.
Maintained by William Ryan. Last updated 1 years ago.
complex-numbersfittinglinear-modelslinear-regressionrobust-statisticsstatistics
1 stars 2.00 score 6 scripts
apedrods
MAINT.Data:Model and Analyse Interval Data
Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix that takes into account the intrinsic nature of interval data, and lead to four different possible configuration structures. The Skew-Normal parameters can be estimated by maximum likelihood, while Normal parameters may be estimated by maximum likelihood or robust trimmed maximum likelihood methods.
Maintained by Pedro Duarte Silva. Last updated 2 years ago.
1.15 score 14 scripts