Showing 200 of total 1363 results (show query)

berndbischl

BBmisc:Miscellaneous Helper Functions for B. Bischl

Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development.

Maintained by Bernd Bischl. Last updated 2 years ago.

24.4 match 20 stars 10.59 score 980 scripts 69 dependents

henrikbengtsson

R.utils:Various Programming Utilities

Utility functions useful when programming and developing R packages.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

16.2 match 63 stars 13.74 score 5.7k scripts 814 dependents

atorus-research

Tplyr:A Traceability Focused Grammar of Clinical Data Summary

A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.

Maintained by Mike Stackhouse. Last updated 1 years ago.

pharmatables

21.8 match 95 stars 9.49 score 138 scripts 2 dependents

yihui

xfun:Supporting Functions for Packages Maintained by 'Yihui Xie'

Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.

Maintained by Yihui Xie. Last updated 2 days ago.

10.0 match 145 stars 18.18 score 916 scripts 4.4k dependents

leonawicz

tabr:Music Notation Syntax, Manipulation, Analysis and Transcription in R

Provides a music notation syntax and a collection of music programming functions for generating, manipulating, organizing, and analyzing musical information in R. Music syntax can be entered directly in character strings, for example to quickly transcribe short pieces of music. The package contains functions for directly performing various mathematical, logical and organizational operations and musical transformations on special object classes that facilitate working with music data and notation. The same music data can be organized in tidy data frames for a familiar and powerful approach to the analysis of large amounts of structured music data. Functions are available for mapping seamlessly between these formats and their representations of musical information. The package also provides an API to 'LilyPond' (<https://lilypond.org/>) for transcribing musical representations in R into tablature ("tabs") and sheet music. 'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. The package generates 'LilyPond' files from R code and can pass them to the 'LilyPond' command line interface to be rendered into sheet music PDF files or inserted into R markdown documents. The package offers nominal MIDI file output support in conjunction with rendering sheet music. The package can read MIDI files and attempts to structure the MIDI data to integrate as best as possible with the data structures and functionality found throughout the package.

Maintained by Matthew Leonawicz. Last updated 6 months ago.

guitar-tablaturelilypondlilypond-apimusic-analysismusic-datamusic-notationmusic-programmingmusic-syntaxmusic-transcriptionsheet-music

21.4 match 132 stars 7.87 score 94 scripts

kurthornik

NLP:Natural Language Processing Infrastructure

Basic classes and methods for Natural Language Processing.

Maintained by Kurt Hornik. Last updated 4 months ago.

15.3 match 6 stars 9.37 score 1.0k scripts 127 dependents

skranz

stringtools:Tools for working with strings in R

Tools for working with strings in R

Maintained by Sebastian Kranz. Last updated 3 years ago.

34.5 match 2 stars 3.66 score 29 scripts 26 dependents

emilhvitfeldt

emoji:Data and Function to Work with Emojis

Contains data about emojis with relevant metadata, and functions to work with emojis when they are in strings.

Maintained by Emil Hvitfeldt. Last updated 5 months ago.

15.6 match 28 stars 7.97 score 304 scripts 3 dependents

janmarvin

openxlsx2:Read, Write and Edit 'xlsx' Files

Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets.

Maintained by Jan Marvin Garbuszus. Last updated 2 days ago.

xlsxcpp

8.1 match 137 stars 13.66 score 194 scripts 11 dependents

rsheets

cellranger:Translate Spreadsheet Cell Ranges to Rows and Columns

Helper functions to work with spreadsheets and the "A1:D10" style of cell range specification.

Maintained by Jennifer Bryan. Last updated 7 years ago.

7.2 match 51 stars 13.84 score 80 scripts 843 dependents

brockk

escalation:A Modular Approach to Dose-Finding Clinical Trials

Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

Maintained by Kristian Brock. Last updated 2 months ago.

12.3 match 15 stars 7.91 score 67 scripts

bioc

annotate:Annotation for microarrays

Using R enviroments for annotation.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotationpathwaysgo

7.0 match 11.41 score 812 scripts 243 dependents

bioc

DeepPINCS:Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences.

Maintained by Dongmin Jung. Last updated 5 months ago.

softwarenetworkgraphandnetworkneuralnetworkopenjdk

16.2 match 4.78 score 4 scripts 2 dependents

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

6.6 match 31 stars 11.46 score 474 scripts 25 dependents

insightsengineering

tern:Create Common TLGs Used in Clinical Trials

Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.

Maintained by Joe Zhu. Last updated 2 months ago.

clinical-trialsgraphslistingsnestoutputstables

5.7 match 79 stars 12.62 score 186 scripts 9 dependents

poissonconsulting

subfoldr2:Save and Load R Objects

Facilitates saving and loading R objects, data frames, tables, plots, text blocks and numbers to subfolders.

Maintained by Joe Thorley. Last updated 13 days ago.

18.9 match 2 stars 3.70 score 5 scripts

rstudio

htmltools:Tools for HTML

Tools for HTML generation and output.

Maintained by Carson Sievert. Last updated 10 months ago.

3.9 match 218 stars 17.61 score 10k scripts 4.5k dependents

vubiostat

yaml:Methods to Convert R Data to YAML and Back

Implements the 'libyaml' 'YAML' 1.1 parser and emitter (<https://pyyaml.org/wiki/LibYAML>) for R.

Maintained by Shawn Garbett. Last updated 3 months ago.

yaml

3.6 match 166 stars 17.74 score 5.2k scripts 5.1k dependents

jl5000

tidyged:Handle GEDCOM Files Using Tidyverse Principles

Create and summarise family tree GEDCOM files using tidy dataframes.

Maintained by Jamie Lendrum. Last updated 3 years ago.

10.0 match 8 stars 5.96 score 23 scripts 3 dependents

bioc

BiocGenerics:S4 generic functions used in Bioconductor

The package defines many S4 generic functions used in Bioconductor.

Maintained by Hervé Pagès. Last updated 1 months ago.

infrastructurebioconductor-packagecore-package

4.1 match 12 stars 14.22 score 612 scripts 2.2k dependents

bioc

Biobase:Biobase: Base functions for Bioconductor

Functions that are needed by many other packages or which replace R functions.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

infrastructurebioconductor-packagecore-package

3.5 match 9 stars 16.45 score 6.6k scripts 1.8k dependents

jkcshea

ivmte:Instrumental Variables: Extrapolation by Marginal Treatment Effects

The marginal treatment effect was introduced by Heckman and Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) <doi:10.2307/2951620>. This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using either linear programming or quadratically constrained quadratic programming. Support for four solvers is provided. Gurobi and the Gurobi R API can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be obtained from <https://www.mosek.com/>. The lp_solve library is freely available from <http://lpsolve.sourceforge.net/5.5/>, and is included when installing its API 'lpSolveAPI', which is available from CRAN.

Maintained by Joshua Shea. Last updated 7 months ago.

10.5 match 18 stars 5.33 score 30 scripts

melff

RKernel:Yet another R kernel for Jupyter

Provides a kernel for Jupyter.

Maintained by Martin Elff. Last updated 14 days ago.

jupyterjupyter-kerneljupyter-kernelsjupyter-notebook

12.0 match 38 stars 4.60 score

qsbase

qs:Quick Serialization of R Objects

Provides functions for quickly writing and reading any R object to and from disk.

Maintained by Travers Ching. Last updated 9 days ago.

compressiondata-storageencodingserializationlibzstdlz4cpp

3.9 match 414 stars 13.91 score 2.5k scripts 51 dependents

mhenderson

llinyn:A Few Esoteric String Operations

A few esoteric string operations in R.

Maintained by Matthew Henderson. Last updated 6 months ago.

string-manipulation

20.6 match 2.48 score 1 dependents

trinker

wakefield:Generate Random Data Sets

Generates random data sets including: data.frames, lists, and vectors.

Maintained by Tyler Rinker. Last updated 5 years ago.

data-generationwakefield

7.0 match 256 stars 7.13 score 209 scripts

truecluster

ff:Memory-Efficient Storage of Large Data on Disk and Fast Access Functions

The ff package provides data structures that are stored on disk but behave (almost) as if they were in RAM by transparently mapping only a section (pagesize) in main memory - the effective virtual memory consumption per ff object. ff supports R's standard atomic data types 'double', 'logical', 'raw' and 'integer' and non-standard atomic types boolean (1 bit), quad (2 bit unsigned), nibble (4 bit unsigned), byte (1 byte signed with NAs), ubyte (1 byte unsigned), short (2 byte signed with NAs), ushort (2 byte unsigned), single (4 byte float with NAs). For example 'quad' allows efficient storage of genomic data as an 'A','T','G','C' factor. The unsigned types support 'circular' arithmetic. There is also support for close-to-atomic types 'factor', 'ordered', 'POSIXct', 'Date' and custom close-to-atomic types. ff not only has native C-support for vectors, matrices and arrays with flexible dimorder (major column-order, major row-order and generalizations for arrays). There is also a ffdf class not unlike data.frames and import/export filters for csv files. ff objects store raw data in binary flat files in native encoding, and complement this with metadata stored in R as physical and virtual attributes. ff objects have well-defined hybrid copying semantics, which gives rise to certain performance improvements through virtualization. ff objects can be stored and reopened across R sessions. ff files can be shared by multiple ff R objects (using different data en/de-coding schemes) in the same process or from multiple R processes to exploit parallelism. A wide choice of finalizer options allows to work with 'permanent' files as well as creating/removing 'temporary' ff files completely transparent to the user. On certain OS/Filesystem combinations, creating the ff files works without notable delay thanks to using sparse file allocation. Several access optimization techniques such as Hybrid Index Preprocessing and Virtualization are implemented to achieve good performance even with large datasets, for example virtual matrix transpose without touching a single byte on disk. Further, to reduce disk I/O, 'logicals' and non-standard data types get stored native and compact on binary flat files i.e. logicals take up exactly 2 bits to represent TRUE, FALSE and NA. Beyond basic access functions, the ff package also provides compatibility functions that facilitate writing code for ff and ram objects and support for batch processing on ff objects (e.g. as.ram, as.ff, ffapply). ff interfaces closely with functionality from package 'bit': chunked looping, fast bit operations and coercions between different objects that can store subscript information ('bit', 'bitwhich', ff 'boolean', ri range index, hi hybrid index). This allows to work interactively with selections of large datasets and quickly modify selection criteria. Further high-performance enhancements can be made available upon request.

Maintained by Jens Oehlschlägel. Last updated 2 months ago.

cpp

3.9 match 27 stars 12.01 score 764 scripts 71 dependents

wraff

wrMisc:Analyze Experimental High-Throughput (Omics) Data

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Maintained by Wolfgang Raffelsberger. Last updated 7 months ago.

9.9 match 4.44 score 33 scripts 4 dependents