Showing 18 of total 18 results (show query)
emmanuelparadis
ape:Analyses of Phylogenetics and Evolution
Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.
Maintained by Emmanuel Paradis. Last updated 5 days ago.
64 stars 17.27 score 13k scripts 599 dependents
bioc
methylKit:DNA methylation analysis from high-throughput bisulfite sequencing results
methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files.
Maintained by Altuna Akalin. Last updated 30 days ago.
dnamethylationsequencingmethylseqgenome-biologymethylationstatistical-analysisvisualizationcurlbzip2xz-utilszlibcpp
220 stars 11.80 score 578 scripts 3 dependents
business-science
tibbletime:Time Aware Tibbles
Built on top of the 'tibble' package, 'tibbletime' is an extension that allows for the creation of time aware tibbles. Some immediate advantages of this include: the ability to perform time-based subsetting on tibbles, quickly summarising and aggregating results by time periods, and creating columns that can be used as 'dplyr' time-based groups.
Maintained by Davis Vaughan. Last updated 4 months ago.
periodicitytibbletimetime-seriestimeseriescpp
177 stars 10.51 score 644 scripts 2 dependents
nicchr
cheapr:Simple Functions to Save Time and Memory
Fast and memory-efficient (or 'cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide 'cheaper' alternatives to common base R functions, as well as some additional functions.
Maintained by Nick Christofides. Last updated 3 days ago.
19 stars 7.21 score 73 scripts 2 dependents
asl
Rssa:A Collection of Methods for Singular Spectrum Analysis
Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, <doi:10.1007/978-3-662-57380-8>). See 'citation("Rssa")' for details.
Maintained by Anton Korobeynikov. Last updated 7 months ago.
58 stars 7.10 score 182 scripts 4 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-detectionartificial-neural-networksautoencodersneural-networksrobust-statisticsopenblascppopenmp
13 stars 5.59 score 60 scripts
ayrangi
WaveletComp:Computational Wavelet Analysis
Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.
Maintained by Angi Roesch. Last updated 7 years ago.
5 stars 5.39 score 181 scripts 5 dependents
rdinnager
slimr:Create, Run and Post-Process 'SLiM' Population Genetics Forward Simulations
Lets you write 'SLiM' scripts (population genomics simulation) using your favourite R IDE, using a syntax as close as possible to the original 'SLiM' language. It offer many tools to manipulate those scripts, as well as run them in the 'SLiM' software from R, as well as capture and post-process their output, after or even during a simulation.
Maintained by Russell Dinnage. Last updated 5 months ago.
8 stars 4.70 score 42 scripts
jmm34
bayess:Bayesian Essentials with R
Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.
Maintained by Jean-Michel Marin. Last updated 1 years ago.
3 stars 4.01 score 68 scripts
magnusdv
pedbuildr:Pedigree Reconstruction
Reconstruct pedigrees from genotype data, by optimising the likelihood over all possible pedigrees subject to given restrictions. Tailor-made plots facilitate evaluation of the output. This package is part of the 'pedsuite' ecosystem for pedigree analysis. In particular, it imports 'pedprobr' for calculating pedigree likelihoods and 'forrel' for estimating pairwise relatedness.
Maintained by Magnus Dehli Vigeland. Last updated 2 months ago.
2 stars 3.78 score 7 scripts 1 dependents
bbuchsbaum
multivarious:Extensible Data Structures for Multivariate Analysis
Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.
Maintained by Bradley Buchsbaum. Last updated 3 months ago.
3.53 score 17 scripts
siyavashshabani
Ghost:Missing Data Segments Imputation in Multivariate Streams
Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.
Maintained by Siyavash Shabani. Last updated 5 years ago.
6 stars 3.26 score 10 scripts
wagner-s
MultIS:Reconstruction of Clones from Integration Site Readouts and Visualization
Tools necessary to reconstruct clonal affiliations from temporally and/or spatially separated measurements of viral integration sites. For this means it utilizes correlations present in the relative readouts of the integration sites. Furthermore, facilities for filtering of the data and visualization of different steps in the pipeline are provided with the package.
Maintained by Sebastian Wagner. Last updated 4 years ago.
2.00 score 1 scripts
cran
EESPCA:Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
Contains logic for computing sparse principal components via the EESPCA method, which is based on an approximation of the eigenvector/eigenvalue identity. Includes logic to support execution of the TPower and rifle sparse PCA methods, as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle via cross-validation to minimize the out-of-sample reconstruction error. H. Robert Frost (2021) <doi:10.1080/10618600.2021.1987254>.
Maintained by H. Robert Frost. Last updated 3 years ago.
1 stars 2.00 scorecran
maxstablePCA:Apply a PCA Like Procedure Suited for Multivariate Extreme Value Distributions
Dimension reduction for multivariate data of extreme events with a PCA like procedure as described in Reinbott, Janßen, (2024), <doi:10.48550/arXiv.2408.10650>. Tools for necessary transformations of the data are provided.
Maintained by Felix Reinbott. Last updated 7 months ago.
1.70 score
dmorinya
MisRepARMA:Misreported Time Series Analysis
Provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <arXiv:2003.09202v2>.
Maintained by David Moriña Soler. Last updated 4 years ago.
1.00 score