Showing 106 of total 106 results (show query)
indrajeetpatil
ggstatsplot:'ggplot2' Based Plots with Statistical Details
Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.
Maintained by Indrajeet Patil. Last updated 1 months ago.
bayes-factorsdatasciencedatavizeffect-sizeggplot-extensionhypothesis-testingnon-parametric-statisticsregression-modelsstatistical-analysis
2.1k stars 14.46 score 3.0k scripts 1 dependents
rstudio
keras3:R Interface to 'Keras'
Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.
Maintained by Tomasz Kalinowski. Last updated 11 days ago.
845 stars 13.63 score 264 scripts 2 dependents
ipums
ipumsr:An R Interface for Downloading, Reading, and Handling IPUMS Data
An easy way to work with census, survey, and geographic data provided by IPUMS in R. Generate and download data through the IPUMS API and load IPUMS files into R with their associated metadata to make analysis easier. IPUMS data describing 1.4 billion individuals drawn from over 750 censuses and surveys is available free of charge from the IPUMS website <https://www.ipums.org>.
Maintained by Derek Burk. Last updated 1 months ago.
30 stars 11.05 score 720 scripts 2 dependents
t-kalinowski
keras:R Interface to 'Keras'
Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Maintained by Tomasz Kalinowski. Last updated 12 months ago.
10.93 score 10k scripts 55 dependentsindrajeetpatil
statsExpressions:Tidy Dataframes and Expressions with Statistical Details
Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.
Maintained by Indrajeet Patil. Last updated 1 months ago.
bayesian-inferencebayesian-statisticscontingency-tablecorrelationeffectsizemeta-analysisparametricrobustrobust-statisticsstatistical-detailsstatistical-teststidy
312 stars 10.92 score 146 scripts 2 dependents
mlverse
luz:Higher Level 'API' for 'torch'
A high level interface for 'torch' providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to support expressing a large range of models. It's heavily inspired by 'fastai' by Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.
Maintained by Daniel Falbel. Last updated 7 months ago.
89 stars 9.86 score 318 scripts 4 dependents
e-sensing
sits:Satellite Image Time Series Analysis for Earth Observation Data Cubes
An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Includes methods to reduce training samples imbalance proposed by Chawla et al (2002) <doi:10.1613/jair.953>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference as described by Camara et al (2024) <doi:10.3390/rs16234572>, and methods for active learning and uncertainty assessment. Supports region-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
Maintained by Gilberto Camara. Last updated 2 months ago.
big-earth-datacbersearth-observationeo-datacubesgeospatialimage-time-seriesland-cover-classificationlandsatplanetary-computerr-spatialremote-sensingrspatialsatellite-image-time-seriessatellite-imagerysentinel-2stac-apistac-catalogcpp
494 stars 9.50 score 384 scripts
bioc
SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf
A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.
Maintained by Lambda Moses. Last updated 2 months ago.
datarepresentationtranscriptomicsspatial
49 stars 9.40 score 322 scripts 1 dependentsmlverse
tabnet:Fit 'TabNet' Models for Classification and Regression
Implements the 'TabNet' model by Sercan O. Arik et al. (2019) <doi:10.48550/arXiv.1908.07442> with 'Coherent Hierarchical Multi-label Classification Networks' by Giunchiglia et al. <doi:10.48550/arXiv.2010.10151> and provides a consistent interface for fitting and creating predictions. It's also fully compatible with the 'tidymodels' ecosystem.
Maintained by Christophe Regouby. Last updated 19 hours ago.
109 stars 9.05 score 65 scriptsbioc
Voyager:From geospatial to spatial omics
SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.
Maintained by Lambda Moses. Last updated 3 months ago.
geneexpressionspatialtranscriptomicsvisualizationbioconductoredaesdaexploratory-data-analysisomicsspatial-statisticsspatial-transcriptomics
88 stars 8.71 score 173 scriptsrstudio
tfprobability:Interface to 'TensorFlow Probability'
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
Maintained by Tomasz Kalinowski. Last updated 3 years ago.
54 stars 8.63 score 221 scripts 3 dependentsbioc
COTAN:COexpression Tables ANalysis
Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes.
Maintained by Galfrè Silvia Giulia. Last updated 1 months ago.
systemsbiologytranscriptomicsgeneexpressionsinglecell
16 stars 7.85 score 96 scripts
eco-hydro
phenofit:Extract Remote Sensing Vegetation Phenology
The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and growing season dividing method and a practical snow elimination method based on Whittaker were proposed. 7 curve fitting methods and 4 phenology extraction methods were provided. Parameters boundary are considered for every curve fitting methods according to their ecological meaning. And 'optimx' is used to select best optimization method for different curve fitting methods. Reference: Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>; Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. <doi:10.1029/2020JG005636>; Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24; Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.
Maintained by Dongdong Kong. Last updated 2 months ago.
phenologyremote-sensingopenblascppopenmp
78 stars 7.71 score 332 scripts
sciviews
SciViews:'SciViews' - Data Processing and Visualization with the 'SciViews::R' Dialect
The 'SciViews::R' dialect provides a set of functions that streamlines data input, process, analysis and visualization especially, but not exclusively, for beginners or occasional users. It mixes base R and tidyverse, plus another set of CRAN packages for an easy and coherent use of R.
Maintained by Philippe Grosjean. Last updated 7 months ago.
8 stars 7.62 score 116 scripts 1 dependentsbioc
CRISPRseek:Design of guide RNAs in CRISPR genome-editing systems
The package encompasses functions to find potential guide RNAs for the CRISPR-based genome-editing systems including the Base Editors and the Prime Editors when supplied with target sequences as input. Users have the flexibility to filter resulting guide RNAs based on parameters such as the absence of restriction enzyme cut sites or the lack of paired guide RNAs. The package also facilitates genome-wide exploration for off-targets, offering features to score and rank off-targets, retrieve flanking sequences, and indicate whether the hits are located within exon regions. All detected guide RNAs are annotated with the cumulative scores of the top5 and topN off-targets together with the detailed information such as mismatch sites and restrictuion enzyme cut sites. The package also outputs INDELs and their frequencies for Cas9 targeted sites.
Maintained by Lihua Julie Zhu. Last updated 21 days ago.
immunooncologygeneregulationsequencematchingcrispr
7.18 score 51 scripts 2 dependentsbioc
scMultiSim:Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions
scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.
Maintained by Hechen Li. Last updated 5 months ago.
singlecelltranscriptomicsgeneexpressionsequencingexperimentaldesign
23 stars 7.08 score 11 scripts
rorynolan
autothresholdr:An R Port of the 'ImageJ' Plugin 'Auto Threshold'
Algorithms for automatically finding appropriate thresholds for numerical data, with special functions for thresholding images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. See <https://imagej.net/plugins/auto-threshold> and Landini et al. (2017) <DOI:10.1111/jmi.12474>.
Maintained by Rory Nolan. Last updated 1 years ago.
6 stars 6.73 score 31 scripts 4 dependents
hubverse-org
hubEnsembles:Ensemble Methods for Combining Hub Model Outputs
Functions for combining model outputs (e.g. predictions or estimates) from multiple models into an aggregated ensemble model output.
Maintained by Li Shandross. Last updated 6 months ago.
6 stars 6.43 score 42 scripts 1 dependents
mateiz
mlflow:Interface to 'MLflow'
R interface to 'MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models.
Maintained by Matei Zaharia. Last updated 16 days ago.
1 stars 6.25 score 644 scriptsbioc
immApex:Tools for Adaptive Immune Receptor Sequence-Based Machine and Deep Learning
A set of tools to build tensorflow/keras3-based models in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem.
Maintained by Nick Borcherding. Last updated 6 days ago.
softwareimmunooncologysinglecellclassificationannotationsequencingmotifannotationcpp
8 stars 5.94 score 3 scriptsbioc
BUSpaRse:kallisto | bustools R utilities
The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices.
Maintained by Lambda Moses. Last updated 5 months ago.
singlecellrnaseqworkflowstepcpp
9 stars 5.87 score 165 scriptssciviews
chart:Unified Interface (with Formula) for R Plots
Chart generalizes plot generation in R, being with base R plot function, lattice or ggplot2. A formula interface is available for both ggplot2 and lattice. The function 'chart()' automatically uses labels and units if they are defined in the data.
Maintained by Philippe Grosjean. Last updated 10 months ago.
4 stars 5.85 score 49 scripts 3 dependentsbioc
sangeranalyseR:sangeranalyseR: a suite of functions for the analysis of Sanger sequence data in R
This package builds on sangerseqR to allow users to create contigs from collections of Sanger sequencing reads. It provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. There is extensive online documentation, and the package can outputs detailed HTML reports, including chromatograms.
Maintained by Kuan-Hao Chao. Last updated 29 days ago.
geneticsalignmentsequencingsangerseqpreprocessingqualitycontrolvisualizationgui
5.76 score 46 scripts
bradleyboehmke
completejourney:Retail Shopping Data
Retail shopping transactions for 2,469 households over one year. Originates from the 84.51° Complete Journey 2.0 source files <https://www.8451.com/area51> which also includes useful metadata on products, coupons, campaigns, and promotions.
Maintained by Brad Boehmke. Last updated 5 years ago.
21 stars 5.70 score 42 scriptsbioc
multicrispr:Multi-locus multi-purpose Crispr/Cas design
This package is for designing Crispr/Cas9 and Prime Editing experiments. It contains functions to (1) define and transform genomic targets, (2) find spacers (4) count offtarget (mis)matches, and (5) compute Doench2016/2014 targeting efficiency. Care has been taken for multicrispr to scale well towards large target sets, enabling the design of large Crispr/Cas9 libraries.
Maintained by Aditya Bhagwat. Last updated 4 months ago.
5.56 score 2 scripts
rebeccasalles
TSPred:Functions for Benchmarking Time Series Prediction
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Maintained by Rebecca Pontes Salles. Last updated 4 years ago.
benchmarkinglinear-modelsmachine-learningnonstationaritytime-series-forecasttime-series-prediction
24 stars 5.53 score 94 scripts 1 dependents
jessesadler
debkeepr:Analysis of Non-Decimal Currencies and Double-Entry Bookkeeping
Analysis of historical non-decimal currencies and value systems that use tripartite or tetrapartite systems such as pounds, shillings, and pence. It introduces new vector classes to represent non-decimal currencies, making them compatible with numeric classes, and provides functions to work with these classes in data frames in the context of double-entry bookkeeping.
Maintained by Jesse Sadler. Last updated 2 years ago.
accountingdigital-humanitieseconomic-historyhistory
9 stars 5.51 score 24 scripts
johncoene
graphTweets:Visualise Twitter Interactions
Allows building an edge table from data frame of tweets, also provides function to build nodes and another create a temporal graph.
Maintained by John Coene. Last updated 5 years ago.
46 stars 5.49 score 67 scripts
inesortega
neuralGAM:Interpretable Neural Network Based on Generalized Additive Models
Neural network framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.
Maintained by Ines Ortega-Fernandez. Last updated 7 months ago.
deep-neural-networksexplainable-aigamganngeneralized-additive-modelsgeneralized-additive-neural-networkself-explanatory-mlxai
2 stars 5.44 score 40 scripts
r-tensorflow
autokeras:R Interface to 'AutoKeras'
R Interface to 'AutoKeras' <https://autokeras.com/>. 'AutoKeras' is an open source software library for Automated Machine Learning (AutoML). The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. 'AutoKeras' provides functions to automatically search for architecture and hyperparameters of deep learning models.
Maintained by Juan Cruz Rodriguez. Last updated 4 years ago.
autodlautomatic-machine-learningautomldeep-learningkerasmachine-learningtensorflow
73 stars 5.34 score
eagerai
tfaddons:Interface to 'TensorFlow SIG Addons'
'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository of community contributions that conform to well-established API patterns, but implement new functionality not available in core 'TensorFlow'. 'TensorFlow' natively supports a large number of operators, layers, metrics, losses, optimizers, and more. However, in a fast moving field like Machine Learning, there are many interesting new developments that cannot be integrated into core 'TensorFlow' (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).
Maintained by Turgut Abdullayev. Last updated 3 years ago.
deep-learningkerasneural-networkstensorflowtensorflow-addonstfa
20 stars 5.20 score 16 scriptsbioc
ttgsea:Tokenizing Text of Gene Set Enrichment Analysis
Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens.
Maintained by Dongmin Jung. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichment
4.95 score 3 scripts 3 dependents
ecodynizw
imageseg:Deep Learning Models for Image Segmentation
A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <arXiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <arXiv:1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.
Maintained by Juergen Niedballa. Last updated 1 years ago.
image-segmentationkerastensorflow
18 stars 4.95 score 9 scripts
diegommcc
SpatialDDLS:Deconvolution of Spatial Transcriptomics Data Based on Neural Networks
Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.
Maintained by Diego Mañanes. Last updated 5 months ago.
deconvolutiondeep-learningneural-networkspatial-transcriptomics
5 stars 4.88 score 1 scripts
ruthkr
deepredeff:Deep Learning Prediction of Effectors
A tool that contains trained deep learning models for predicting effector proteins. 'deepredeff' has been trained to identify effector proteins using a set of known experimentally validated effectors from either bacteria, fungi, or oomycetes. Documentation is available via several vignettes, and the paper by Kristianingsih and MacLean (2020) <doi:10.1101/2020.07.08.193250>.
Maintained by Ruth Kristianingsih. Last updated 2 years ago.
4 stars 4.86 score 18 scripts
daranzolin
hacksaw:Additional Tools for Splitting and Cleaning Data
Move between data frames and lists more efficiently with precision splitting via 'dplyr' verbs. Easily cast variables to different data types. Keep rows with NAs. Shift row values.
Maintained by David Ranzolin. Last updated 4 years ago.
34 stars 4.84 score 41 scriptsbioc
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
4.78 score 4 scripts 2 dependents
hugheylab
limorhyde2:Quantify Rhythmicity and Differential Rhythmicity in Genomic Data
Fit linear models based on periodic splines, moderate model coefficients using multivariate adaptive shrinkage, then compute properties of the moderated curves.
Maintained by Jake Hughey. Last updated 1 years ago.
4.78 score 2 scripts
geomarker-io
addr:Clean, Parse, Harmonize, Match, and Geocode Messy Real-World Addresses
Addresses that were not validated at the time of collection are often heterogenously formatted, making them difficult to compare or link to other sets of addresses. The addr package is designed to clean character strings of addresses, use the `usaddress` library to tag address components, and paste together select components to create a normalized address. Normalized addresses can be hashed to create hashdresses that can be used to merge with other sets of addresses.
Maintained by Cole Brokamp. Last updated 5 months ago.
2 stars 4.70 score 388 scripts
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 scriptssciviews
tabularise:Create Tabular Outputs from R
Create rich-formatted tabular outputs from R that can be incorporated into R Markdown/Quarto documents with correct output at least in HTML, LaTeX/PDF, Word and PowerPoint formats for various R objects.
Maintained by Philippe Grosjean. Last updated 10 months ago.
4.56 score 12 scripts 4 dependentsmlverse
tfevents:Write Events for 'TensorBoard'
Provides a convenient way to log scalars, images, audio, and histograms in the 'tfevent' record file format. Logged data can be visualized on the fly using 'TensorBoard', a web based tool that focuses on visualizing the training progress of machine learning models.
Maintained by Daniel Falbel. Last updated 9 months ago.
10 stars 4.48 score 10 scripts
nhejazi
medoutcon:Efficient Natural and Interventional Causal Mediation Analysis
Efficient estimators of interventional (in)direct effects in the presence of mediator-outcome confounding affected by exposure. The effects estimated allow for the impact of the exposure on the outcome through a direct path to be disentangled from that through mediators, even in the presence of intermediate confounders that complicate such a relationship. Currently supported are non-parametric efficient one-step and targeted minimum loss estimators based on the formulation of Díaz, Hejazi, Rudolph, and van der Laan (2020) <doi:10.1093/biomet/asaa085>. Support for efficient estimation of the natural (in)direct effects is also provided, appropriate for settings in which intermediate confounders are absent. The package also supports estimation of these effects when the mediators are measured using outcome-dependent two-phase sampling designs (e.g., case-cohort).
Maintained by Nima Hejazi. Last updated 1 years ago.
causal-inferencecausal-machine-learninginverse-probability-weightsmachine-learningmediation-analysisstochastic-interventionstargeted-learningtreatment-effects
13 stars 4.46 score 22 scripts
neural-structured-additive-learning
deeptrafo:Fitting Deep Conditional Transformation Models
Allows for the specification of deep conditional transformation models (DCTMs) and ordinal neural network transformation models, as described in Baumann et al (2021) <doi:10.1007/978-3-030-86523-8_1> and Kook et al (2022) <doi:10.1016/j.patcog.2021.108263>. Extensions such as autoregressive DCTMs (Ruegamer et al, 2023, <doi:10.1007/s11222-023-10212-8>) and transformation ensembles (Kook et al, 2022, <doi:10.48550/arXiv.2205.12729>) are implemented. The software package is described in Kook et al (2024, <doi:10.18637/jss.v111.i10>).
Maintained by Lucas Kook. Last updated 2 months ago.
5 stars 4.44 score 11 scriptssciviews
svBase:Base Objects like Data Frames for 'SciViews::R'
Functions to manipulated the three main classes of "data frames" for 'SciViews::R': data.frame, data.table and tibble. Allow to select the preferred one, and to convert more carefully between the three, taking care of correct presentation of row names and data.table's keys. More homogeneous way of creating these three data frames and of printing them on the R console.
Maintained by Philippe Grosjean. Last updated 9 months ago.
4.38 score 3 scripts 8 dependentssciviews
data.io:Read and Write Data in Different Formats
Read or write data from many different formats (tabular datasets, from statistic software ...) into R objects. Add labels and units in different languages.
Maintained by Philippe Grosjean. Last updated 11 months ago.
1 stars 4.32 score 20 scripts 7 dependents
jiaxiangbu
add2ggplot:Add to 'ggplot2'
Create 'ggplot2' themes and color palettes.
Maintained by Jiaxiang Li. Last updated 5 years ago.
4 stars 4.30 score 8 scriptsbioc
SpatialCPie:Cluster analysis of Spatial Transcriptomics data
SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues.
Maintained by Joseph Bergenstraahle. Last updated 5 months ago.
transcriptomicsclusteringrnaseqsoftware
4.30 score 2 scriptsbioc
GUIDEseq:GUIDE-seq and PEtag-seq analysis pipeline
The package implements GUIDE-seq and PEtag-seq analysis workflow including functions for filtering UMI and reads with low coverage, obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites with mismatches and indels.
Maintained by Lihua Julie Zhu. Last updated 5 months ago.
immunooncologygeneregulationsequencingworkflowstepcrispr
4.23 score 14 scripts
bioc
orthos:`orthos` is an R package for variance decomposition using conditional variational auto-encoders
`orthos` decomposes RNA-seq contrasts, for example obtained from a gene knock-out or compound treatment experiment, into unspecific and experiment-specific components. Original and decomposed contrasts can be efficiently queried against a large database of contrasts (derived from ARCHS4, https://maayanlab.cloud/archs4/) to identify similar experiments. `orthos` furthermore provides plotting functions to visualize the results of such a search for similar contrasts.
Maintained by Panagiotis Papasaikas. Last updated 19 days ago.
rnaseqdifferentialexpressiongeneexpression
4.18 score 2 scripts
elray1
distfromq:Reconstruct a Distribution from a Collection of Quantiles
Given a set of predictive quantiles from a distribution, estimate the distribution and create `d`, `p`, `q`, and `r` functions to evaluate its density function, distribution function, and quantile function, and generate random samples. On the interior of the provided quantiles, an interpolation method such as a monotonic cubic spline is used; the tails are approximated by a location-scale family.
Maintained by Evan Ray. Last updated 7 months ago.
4.05 score 37 scripts 2 dependentshubverse-org
hubverse:Easily Install and Load the 'hubverse'
A collection of packages that enables collaborative modeling exercises through a unified framework for aggregating, visualizing, and evaluating forecasts. This package is designed to make it easy to install and load multiple 'hubverse' packages in a single step. Learn more at <https://hubverse.io/en/latest/index.html>.
Maintained by Li Shandross. Last updated 6 months ago.
4.00 score 5 scriptsbioc
VAExprs:Generating Samples of Gene Expression Data with Variational Autoencoders
A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones.
Maintained by Dongmin Jung. Last updated 5 months ago.
softwaregeneexpressionsinglecellopenjdk
4.00 score 4 scripts
semran9
tfNeuralODE:Create Neural Ordinary Differential Equations with 'tensorflow'
Provides a framework for the creation and use of Neural ordinary differential equations with the 'tensorflow' and 'keras' packages. The idea of Neural ordinary differential equations comes from Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and presents a novel way of learning and solving differential systems.
Maintained by Shayaan Emran. Last updated 1 years ago.
1 stars 4.00 score 5 scripts
ashesitr
reservr:Fit Distributions and Neural Networks to Censored and Truncated Data
Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) <doi:10.1007/s13385-022-00314-4>.
Maintained by Alexander Rosenstock. Last updated 9 months ago.
4 stars 3.78 score 9 scripts
xytangtang
ProcData:Process Data Analysis
Provides tools for exploratory process data analysis. Process data refers to the data describing participants' problem-solving processes in computer-based assessments. It is often recorded in computer log files. This package provides functions to read, process, and write process data. It also implements two feature extraction methods to compress the information stored in process data into standard numerical vectors. This package also provides recurrent neural network based models that relate response processes with other binary or scale variables of interest. The functions that involve training and evaluating neural networks are wrappers of functions in 'keras'.
Maintained by Xueying Tang. Last updated 4 years ago.
10 stars 3.70 score 2 scripts
sharifrahmanie
MBMethPred:Medulloblastoma Subgroups Prediction
Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, 'MBMethPred' can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. <doi: 10.3389/fgene.2023.1233657> for more details.
Maintained by Edris Sharif Rahmani. Last updated 2 years ago.
3.70 score 1 scripts
vasileioskarapoulios
LDNN:Longitudinal Data Neural Network
This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.
Maintained by Vasileios Karapoulios. Last updated 4 years ago.
3.70 score 6 scriptssciviews
modelit:Statistical Models for 'SciViews::R'
Create and use statistical models (linear, general, nonlinear...) with extensions to support rich-formatted tables, equations and plots for the 'SciViews::R' dialect.
Maintained by Philippe Grosjean. Last updated 4 months ago.
1 stars 3.30 score 8 scripts
avm00016
predtoolsTS:Time Series Prediction Tools
Makes the time series prediction easier by automatizing this process using four main functions: prep(), modl(), pred() and postp(). Features different preprocessing methods to homogenize variance and to remove trend and seasonality. Also has the potential to bring together different predictive models to make comparatives. Features ARIMA and Data Mining Regression models (using caret).
Maintained by Alberto Vico Moreno. Last updated 7 years ago.
1 stars 3.20 score 32 scriptsbioc
NeuCA:NEUral network-based single-Cell Annotation tool
NeuCA is is a neural-network based method for scRNA-seq data annotation. It can automatically adjust its classification strategy depending on cell type correlations, to accurately annotate cell. NeuCA can automatically utilize the structure information of the cell types through a hierarchical tree to improve the annotation accuracy. It is especially helpful when the data contain closely correlated cell types.
Maintained by Hao Feng. Last updated 5 days ago.
singlecellsoftwareclassificationneuralnetworkrnaseqtranscriptomicsdatarepresentationtranscriptionsequencingpreprocessinggeneexpressiondataimport
3.18 score 3 scripts
jaipizgon
TSLSTMplus:Long-Short Term Memory for Time-Series Forecasting, Enhanced
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
Maintained by Jaime Pizarroso Gonzalo. Last updated 2 months ago.
3.18 score 1 scripts
b-thi
FuncNN:Functional Neural Networks
A collection of functions which fit functional neural network models. In other words, this package will allow users to build deep learning models that have either functional or scalar responses paired with functional and scalar covariates. We implement the theoretical discussion found in Thind, Multani and Cao (2020) <arXiv:2006.09590> through the help of a main fitting and prediction function as well as a number of helper functions to assist with cross-validation, tuning, and the display of estimated functional weights.
Maintained by Barinder Thind. Last updated 5 years ago.
3 stars 3.18 score 5 scriptsbioc
DeProViR:A Deep-Learning Framework Based on Pre-trained Sequence Embeddings for Predicting Host-Viral Protein-Protein Interactions
Emerging infectious diseases, exemplified by the zoonotic COVID-19 pandemic caused by SARS-CoV-2, are grave global threats. Understanding protein-protein interactions (PPIs) between host and viral proteins is essential for therapeutic targets and insights into pathogen replication and immune evasion. While experimental methods like yeast two-hybrid screening and mass spectrometry provide valuable insights, they are hindered by experimental noise and costs, yielding incomplete interaction maps. Computational models, notably DeProViR, predict PPIs from amino acid sequences, incorporating semantic information with GloVe embeddings. DeProViR employs a Siamese neural network, integrating convolutional and Bi-LSTM networks to enhance accuracy. It overcomes the limitations of feature engineering, offering an efficient means to predict host-virus interactions, which holds promise for antiviral therapies and advancing our understanding of infectious diseases.
Maintained by Matineh Rahmatbakhsh. Last updated 5 days ago.
proteomicssystemsbiologynetworkinferenceneuralnetworknetwork
1 stars 3.18 score 1 scripts
gertjanssenswillen
processpredictR:Process Prediction
Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple ways to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <arXiv:2104.00721>.
Maintained by Gert Janssenswillen. Last updated 2 years ago.
3.15 score 28 scripts
urswilke
pyramidi:Generate and Manipulate Midi Data in R Data Frames
Import the python libraries miditapyr and mido to read in midi file data in pandas DataFrames. These can then be imported in R via reticulate. The event-based midi data is widened to facilitate the manipulation and plotting of note-based structures as in music21. The data frame format allows for an easy implementation of many music data manipulations.
Maintained by Urs Wilke. Last updated 1 years ago.
8 stars 3.03 score 27 scripts
hughjonesd
apicheck:Explore the Historical API of R Packages
Check when functions were introduced and/or APIs changed in packages, using the 'MRAN' service.
Maintained by David Hugh-Jones. Last updated 6 years ago.
21 stars 3.02 score 5 scriptssciviews
inferit:Hypothesis Tests and Statistical Distributions for 'SciViews::R'
Statistical distributions (including their visual representation) and hypothesis tests with rich-formatted tabular outputs for the 'SciViews::R' dialect.
Maintained by Philippe Grosjean. Last updated 10 months ago.
sciviewsstatistical-inferencestatistical-tests
3.00 score 6 scripts
learnitr
learnitgrid:Manage Rubrics or Assessment Grids for GitHub Repositories
Create and manage semi-automatically rubrics to assess GitHub projects (R scripts, R Markdown or Quarto files). Create directed projects where students have to complete documents and submit them to GitHub (classroom) so that they are evaluated using the rubric (or assessment grid).
Maintained by Philippe Grosjean. Last updated 10 months ago.
1 stars 3.00 score 7 scripts
travis-barton
LilRhino:For Implementation of Feed Reduction, Learning Examples, NLP and Code Management
This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.
Maintained by Travis Barton. Last updated 3 years ago.
1 stars 2.78 score 12 scripts
egr95
codacore:Learning Sparse Log-Ratios for Compositional Data
In the context of high-throughput genetic data, CoDaCoRe identifies a set of sparse biomarkers that are predictive of a response variable of interest (Gordon-Rodriguez et al., 2021) <doi:10.1093/bioinformatics/btab645>. More generally, CoDaCoRe can be applied to any regression problem where the independent variable is Compositional (CoDa), to derive a set of scale-invariant log-ratios (ILR or SLR) that are maximally associated to a dependent variable.
Maintained by Elliott Gordon-Rodriguez. Last updated 3 years ago.
2.70 score 2 scripts
pboutros
iSubGen:Integrative Subtype Generation
Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations.
Maintained by Paul C Boutros. Last updated 4 years ago.
2.70 score 4 scriptssciviews
exploreit:Exploratory Data Analysis for 'SciViews::R'
Multivariate analysis and data exploration for the 'SciViews::R' dialect.
Maintained by Philippe Grosjean. Last updated 11 months ago.
multivariate-analysissciviewsstatistical-methods
2.70 score 4 scripts
shixiangwang
sigminer.prediction:Train and Predict Cancer Subtype with Keras Model based on Mutational Signatures
Mutational signatures represent mutational processes occured in cancer evolution, thus are stable and genetic resources for subtyping. This tool provides functions for training neutral network models to predict the subtype a sample belongs to based on 'keras' and 'sigminer' packages.
Maintained by Shixiang Wang. Last updated 3 years ago.
kerasmutational-signaturesprostate-cancersigminer
8 stars 2.60 score 2 scripts
elipousson
ipumseasyr:Easy Access to IPUMS Data
A package with helper functions extending the ipumsr package for accessing NHGIS and other IPUMS data sources.
Maintained by Eli Pousson. Last updated 6 months ago.
2.40 score 2 scripts
druegamer
deepregression:Fitting Deep Distributional Regression
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>. Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
Maintained by David Ruegamer. Last updated 4 months ago.
2.28 score 63 scripts 1 dependents
economic
epidatatools:Data tools used at the Economic Policy Institute
Tools used by the Economic Policy Institute.
Maintained by Ben Zipperer. Last updated 4 months ago.
1 stars 2.26 score 18 scripts
fchianucci
hemispheR:Processing Hemispherical Canopy Images
Import and classify canopy fish-eye images, estimate angular gap fraction and derive canopy attributes like leaf area index and openness. Additional information is provided in the study by Chianucci F., Macek M. (2023) <doi:10.1016/j.agrformet.2023.109470>.
Maintained by Francesco Chianucci. Last updated 12 months ago.
3 stars 2.18 score 7 scripts
bips-hb
survnet:Artificial neural networks for survival analysis
Artificial neural networks for survival analysis
Maintained by Marvin N. Wright. Last updated 4 years ago.
2 stars 2.00 score 9 scripts
promidat
forecasteR:Time Series Forecast System
A web application for displaying, analysing and forecasting univariate time series. Includes basic methods such as mean, naïve, seasonal naïve and drift, as well as more complex methods such as Holt-Winters Box,G and Jenkins, G (1976) <doi:10.1111/jtsa.12194> and ARIMA Brockwell, P.J. and R.A.Davis (1991) <doi:10.1007/978-1-4419-0320-4>.
Maintained by Oldemar Rodriguez. Last updated 2 years ago.
2.00 score 2 scripts
pigian
snap:Simple Neural Application
A simple wrapper to easily design vanilla deep neural networks using 'Tensorflow'/'Keras' backend for regression, classification and multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding, feature selection and anomaly detection).
Maintained by Giancarlo Vercellino. Last updated 4 years ago.
2.00 score
converseg
ML2Pvae:Variational Autoencoder Models for IRT Parameter Estimation
Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.
Maintained by Geoffrey Converse. Last updated 3 years ago.
2.00 score 4 scriptspigian
janus:Optimized Recommending System Based on 'tensorflow'
Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using 'tensorflow'.
Maintained by Giancarlo Vercellino. Last updated 2 years ago.
1.81 score 65 scripts
cran
roseRF:ROSE Random Forests for Robust Semiparametric Efficient Estimation
ROSE (RObust Semiparametric Efficient) random forests for robust semiparametric efficient estimation in partially parametric models (containing generalised partially linear models). Details can be found in the paper by Young and Shah (2024) <doi:10.48550/arXiv.2410.03471>.
Maintained by Elliot H. Young. Last updated 6 months ago.
1.70 scorecran
TraceAssist:Nonparametric Trace Regression via Sign Series Representation
Efficient method for fitting nonparametric matrix trace regression model. The detailed description can be found in C. Lee, L. Li, H. Zhang, and M. Wang (2021). Nonparametric Trace Regression via Sign Series Representation. <arXiv:2105.01783>. The method employs the aggregation of structured sign series for trace regression (ASSIST) algorithm.
Maintained by Chanwoo Lee. Last updated 4 years ago.
1.70 score
ranjitstat
TSLSTM:Long Short Term Memory (LSTM) Model for Time Series Forecasting
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
Maintained by Dr. Ranjit Kumar Paul. Last updated 3 years ago.
1.48 score 4 scripts 1 dependentscran
WMAP:Weighted Meta-Analysis with Pseudo-Populations
Implementation of integrative weighting approaches for multiple observational studies and causal inferences. The package features three weighting approaches, each representing a special case of the unified weighting framework, introduced by Guha and Li (2024) <doi:10.1093/biomtc/ujae070>, which includes an extension of inverse probability weights for data integration settings.
Maintained by Subharup Guha. Last updated 4 months ago.
1.30 score
mariushofert
gnn:Generative Neural Networks
Tools to set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided.
Maintained by Marius Hofert. Last updated 1 years ago.
1.15 score 14 scripts
pigian
codez:Seq2Seq Encoder-Decoder Model for Time-Feature Analysis Based on Tensorflow
Proposes Seq2seq Time-Feature Analysis using an Encoder-Decoder to project into latent space and a Forward Network to predict the next sequence.
Maintained by Giancarlo Vercellino. Last updated 3 years ago.
1.00 score 1 scripts
willzywiec
criticality:Modeling Fissile Material Operations in Nuclear Facilities
A collection of functions for modeling fissile material operations in nuclear facilities, based on Zywiec et al (2021) <doi:10.1016/j.ress.2020.107322>.
Maintained by William Zywiec. Last updated 2 years ago.
1.00 scorecran
SPORTSCausal:Spillover Time Series Causal Inference
A time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. 'SPORTSCausal' (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on 'CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.
Maintained by Feiyu Yue. Last updated 4 years ago.
1.00 score
yeasinstat
WaveletLSTM:Wavelet Based LSTM Model
A wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) <DOI:10.1007/s00521-017-3289-9> <doi:10.1007/s00500-021-06087-4>.
Maintained by Dr. Md Yeasin. Last updated 2 years ago.
1.00 score
kapiliasri
EEMDlstm:EEMD Based LSTM Model for Time Series Forecasting
Forecasting univariate time series with ensemble empirical mode decomposition (EEMD) with long short-term memory (LSTM). For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
Maintained by Kapil Choudhary. Last updated 3 years ago.
1.00 score