R-universe search: exports:decode

bioc

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.

infrastructure datarepresentation bioconductor-package core-package

18 stars 16.05 score 1.0k scripts 1.9k dependents

mhahsler

arules:Mining Association Rules and Frequent Itemsets

Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. Hahsler, Gruen and Hornik (2005) <doi:10.18637/jss.v014.i15>.

Maintained by Michael Hahsler. Last updated 2 months ago.

arules association-rules frequent-itemsets

194 stars 13.99 score 3.3k scripts 28 dependents

bioc

IPO:Automated Optimization of XCMS Data Processing parameters

The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models.

Maintained by Thomas Lieb. Last updated 5 months ago.

immunooncology metabolomics massspectrometry

34 stars 8.14 score 41 scripts

symbolixau

googlePolylines:Encoding Coordinates into 'Google' Polylines

Encodes simple feature ('sf') objects and coordinates, and decodes polylines using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).

Maintained by David Cooley. Last updated 15 days ago.

geospatial gis google-maps polyline-encoder r-spatial spatial cpp

18 stars 8.11 score 9 dependents

hturner

PlackettLuce:Plackett-Luce Models for Rankings

Functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings may be handled by adding pseudo-rankings with a hypothetical item. Optionally, a multivariate normal prior may be set on the log-worth parameters and ranker reliabilities may be incorporated as proposed by Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a posteriori estimation is used when priors are set. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for further details.

Maintained by Heather Turner. Last updated 2 years ago.

plackett-luce-models preferences ranking rankings-data statistical-models

20 stars 7.97 score 86 scripts 3 dependents

polmine

polmineR:Verbs and Nouns for Corpus Analysis

Package for corpus analysis using the Corpus Workbench ('CWB', <https://cwb.sourceforge.io>) as an efficient back end for indexing and querying large corpora. The package offers functionality to flexibly create subcorpora and to carry out basic statistical operations (count, co-occurrences etc.). The original full text of documents can be reconstructed and inspected at any time. Beyond that, the package is intended to serve as an interface to packages implementing advanced statistical procedures. Respective data structures (document-term matrices, term-co-occurrence matrices etc.) can be created based on the indexed corpora.

Maintained by Andreas Blaette. Last updated 1 years ago.

49 stars 7.96 score 311 scripts

kwb-r

kwb.utils:General Utility Functions Developed at KWB

This package contains some small helper functions that aim at improving the quality of code developed at Kompetenzzentrum Wasser gGmbH (KWB).

Maintained by Hauke Sonnenberg. Last updated 1 years ago.

8 stars 7.33 score 12 scripts 78 dependents

jeroen

base64:Base64 Encoder and Decoder

Compatibility wrapper to replace the orphaned package. New applications should use base64 encoders from 'jsonlite' or 'openssl' or 'base64enc'.

Maintained by Jeroen Ooms. Last updated 6 months ago.

2 stars 6.62 score 163 scripts 42 dependents

extendr

b64:Fast and Vectorized Base 64 Engine

Provides a fast, lightweight, and vectorized base 64 engine to encode and decode character and raw vectors as well as files stored on disk. Common base 64 alphabets are supported out of the box including the standard, URL-safe, bcrypt, crypt, 'BinHex', and IMAP-modified UTF-7 alphabets. Custom engines can be created to support unique base 64 encoding and decoding needs.

Maintained by Josiah Parry. Last updated 2 months ago.

rust cargo

16 stars 6.03 score 4 scripts 3 dependents

rapler

dst:Using the Theory of Belief Functions

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

Maintained by Peiyuan Zhu. Last updated 13 hours ago.

cpp

6 stars 5.98 score 126 scripts

henrikbengtsson

aroma.affymetrix:Analysis of Large Affymetrix Microarray Data Sets

A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

infrastructure proprietaryplatforms exonarray microarray onechannel gui dataimport datarepresentation preprocessing qualitycontrol visualization reportwriting acgh copynumbervariants differentialexpression geneexpression snp transcription affymetrix analysis copy-number dna expression hpc large-scale notebook reproducibility rna

10 stars 5.79 score 112 scripts 3 dependents

munterfi

flexpolyline:Flexible Polyline Encoding

Binding to the C++ implementation of the flexible polyline encoding by HERE <https://github.com/heremaps/flexible-polyline>. The flexible polyline encoding is a lossy compressed representation of a list of coordinate pairs or coordinate triples. The encoding is achieved by: (1) Reducing the decimal digits of each value; (2) encoding only the offset from the previous point; (3) using variable length for each coordinate delta; and (4) using 64 URL-safe characters to display the result.

Maintained by Merlin Unterfinger. Last updated 2 years ago.

gis heremaps polyline polyline-decoder polyline-encoder rspatial cpp

9 stars 5.75 score 14 scripts 1 dependents

bflammers

ANN2:Artificial Neural Networks for Anomaly Detection

Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning.

Maintained by Bart Lammers. Last updated 4 years ago.

anomaly-detection artificial-neural-networks autoencoders neural-networks robust-statistics openblas cpp openmp

13 stars 5.59 score 60 scripts

bquast

HomomorphicEncryption:BFV, BGV, CKKS Schema for Fully Homomorphic Encryption

Implements the Brakerski-Fan-Vercauteren (BFV, 2012) <https://eprint.iacr.org/2012/144>, Brakerski-Gentry-Vaikuntanathan (BGV, 2014) <doi:10.1145/2633600>, and Cheon-Kim-Kim-Song (CKKS, 2016) <https://eprint.iacr.org/2016/421.pdf> schema for Fully Homomorphic Encryption. The included vignettes demonstrate the encryption procedures.

Maintained by Bastiaan Quast. Last updated 1 years ago.

1 stars 5.52 score 39 scripts

chris31415926535

valhallr:A Tidy Interface to the 'Valhalla' Routing Engine

An interface to the 'Valhalla' routing engine’s application programming interfaces (APIs) for turn-by-turn routing, isochrones, and origin-destination analyses. Also includes several user-friendly functions for plotting outputs, and strives to follow "tidy" design principles. Please note that this package requires access to a running instance of 'Valhalla', which is open source and can be downloaded from <https://github.com/valhalla/valhalla>.

Maintained by Christopher Belanger. Last updated 3 years ago.

12 stars 5.21 score 27 scripts

myominnoo

mStats:Medical Statistics & Epidemiological Analysis

A set of tidyverse-friendly functions for data management, calculation of epidemiological measures, statistical analysis, and table creation.

Maintained by Myo Minn Oo. Last updated 1 years ago.

data-management epidemiological-calculations medical-statistics

4.98 score 16 scripts 1 dependents

olisansonwu

diyar:Record Linkage and Epidemiological Case Definitions in 'R'

An R package for iterative and batched record linkage, and applying epidemiological case definitions. 'diyar' can be used for deterministic and probabilistic record linkage, or multistage record linkage combining both approaches. It features the implementation of nested match criteria, and mechanisms to address missing data and conflicting matches during stepwise record linkage. Case definitions are implemented by assigning records to groups based on match criteria such as person or place, and overlapping time or duration of events e.g. sample collection dates or periods of hospital stays. Matching records are assigned a unique group ID. Index and duplicate records are removed or further analyses as required.

Maintained by Olisaeloka Nsonwu. Last updated 3 months ago.

6 stars 4.77 score 33 scripts

alshum

hashids:Generate Short Unique YouTube-Like IDs (Hashes) from Integers

An R port of the hashids library. hashids generates YouTube-like hashes from integers or vector of integers. Hashes generated from integers are relatively short, unique and non-seqential. hashids can be used to generate unique ids for URLs and hide database row numbers from the user. By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other. hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.

Maintained by Alex Shum. Last updated 6 years ago.

18 stars 4.10 score 14 scripts

bergsmat

encode:Represent Ordered Lists and Pairs as Strings

Interconverts between ordered lists and compact string notation. Useful for capturing code lists, and pair-wise codes and decodes, for text storage. Analogous to factor levels and labels. Generics encode() and decode() perform interconversion, while codes() and decodes() extract components of an encoding. The function encoded() checks whether something is interpretable as an encoding. If a vector has an encoded 'guide' attribute, as_factor() uses it to coerce to factor.

Maintained by Tim Bergsma. Last updated 6 years ago.

2 stars 4.03 score 12 scripts 5 dependents

eribul

decoder:Decode Coded Variables to Plain Text and the Other Way Around

Main function "decode" is used to decode coded key values to plain text. Function "code" can be used to code plain text to code if there is a 1:1 relation between the two. The concept relies on 'keyvalue' objects used for translation. There are several 'keyvalue' objects included in the areas of geographical regional codes, administrative health care unit codes, diagnosis codes and more. It is also easy to extend the use by arbitrary code sets.

Maintained by Erik Bulow. Last updated 5 years ago.

3.98 score 16 scripts 1 dependents

barryzee

clickableImageMap:Implement 'tableGrob' Object as a Clickable Image Map

Implement 'tableGrob' object as a clickable image map. The 'clickableImageMap' package is designed to be more convenient and more configurable than the edit() function. Limitations that I have encountered with edit() are cannot control (1) positioning (2) size (3) appearance and formatting of fonts In contrast, when the table is implemented as a 'tableGrob', all of these features are controllable. In particular, the 'ggplot2' grid system allows exact positioning of the table relative to other graphics etc.

Maintained by Barry Zeeberg. Last updated 11 months ago.

2.00 score

daniellequinn

secrettext:Encrypt Text Using a Shifting Substitution Cipher

Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption.

Maintained by Danielle Quinn. Last updated 5 years ago.

2.00 score 1 scripts

cran

gausscov:The Gaussian Covariate Method for Variable Selection

The standard linear regression theory whether frequentist or Bayesian is based on an 'assumed (revealed?) truth' (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper, it is intuitive and requires only four simple equations. Its overwhelming advantage compared with a standard F P-value is that is is exact and valid whatever the data. In contrast F P-values are only valid for specially designed simulations. Given this a valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using six real data sets, four from high dimensional regression and two from vector autoregression. The simplicity and superiority of Gaussian P-values derive from their universal exactness and validity. This is in complete contrast to standard F P-values which are valid only for carefully designed simulations. The function f1st is the most important function. It is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the web site below and the accompanying papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", <doi:10.48550/arXiv.2202.01553>, L. Davies, "An Approximation Based Theory of Linear Regression", 2024, <doi:10.48550/arXiv.2402.09858>.

Maintained by Laurie Davies. Last updated 1 months ago.

fortran

2 stars 1.78 score

cspun

rDecode:Descent-Based Calibrated Optimal Direct Estimation

Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).

Maintained by Chi Seng Pun. Last updated 5 years ago.

1.00 score

cran

adana:Adaptive Nature-Inspired Algorithms for Hybrid Genetic Optimization

The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package 'adana' includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).

Maintained by Erkut Tekeli. Last updated 3 years ago.

1.00 score