Showing 23 of total 23 results (show query)
pharmacologie-caen
vigicaen:'VigiBase' Pharmacovigilance Database Toolbox
Perform the analysis of the World Health Organization (WHO) Pharmacovigilance database 'VigiBase' (Extract Case Level version), <https://who-umc.org/> e.g., load data, perform data management, disproportionality analysis, and descriptive statistics. Intended for pharmacovigilance routine use or studies. This package is NOT supported nor reflect the opinion of the WHO, or the Uppsala Monitoring Centre. Disproportionality methods are described by Norén et al (2013) <doi:10.1177/0962280211403604>.
Maintained by Charles Dolladille. Last updated 2 days ago.
datamanagementpharmacovigilance
20.5 match 1 stars 6.27 score 11 scripts
cran
chkptstanr:Checkpoint MCMC Sampling with 'Stan'
Fit Bayesian models in Stan <doi: 10.18637/jss.v076.i01> with checkpointing, that is, the ability to stop the MCMC sampler at will, and then pick right back up where the MCMC sampler left off. Custom 'Stan' models can be fitted, or the popular package 'brms' <doi: 10.18637/jss.v080.i01> can be used to generate the 'Stan' code. This package is fully compatible with the R packages 'brms', 'posterior', 'cmdstanr', and 'bayesplot'.
Maintained by Donald Williams. Last updated 3 years ago.
28.5 match 2 stars 3.72 score 26 scripts
predictiveecology
SpaDES.core:Core Utilities for Developing and Running Spatially Explicit Discrete Event Models
Provides the core framework for a discrete event system to implement a complete data-to-decisions, reproducible workflow. The core components facilitate the development of modular pieces, and enable the user to include additional functionality by running user-built modules. Includes conditional scheduling, restart after interruption, packaging of reusable modules, tools for developing arbitrary automated workflows, automated interweaving of modules of different temporal resolution, and tools for visualizing and understanding the within-project dependencies. The suggested package 'NLMR' can be installed from the repository (<https://PredictiveEcology.r-universe.dev>).
Maintained by Eliot J B McIntire. Last updated 17 days ago.
discrete-events-simulationssimulation-frameworksimulation-modeling
6.0 match 10 stars 10.61 score 142 scripts 6 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 6 months ago.
6.0 match 89 stars 9.88 score 318 scripts 4 dependents
sparklyr
sparklyr:R Interface to Apache Spark
R interface to Apache Spark, a fast and general engine for big data processing, see <https://spark.apache.org/>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
Maintained by Edgar Ruiz. Last updated 8 days ago.
apache-sparkdistributeddplyridelivymachine-learningremote-clusterssparksparklyr
3.8 match 959 stars 15.16 score 4.0k scripts 21 dependents
rstudio
tfestimators:Interface to 'TensorFlow' Estimators
Interface to 'TensorFlow' Estimators <https://www.tensorflow.org/guide/estimator>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.
Maintained by Tomasz Kalinowski. Last updated 3 years ago.
4.9 match 57 stars 8.42 score 170 scripts
marcellgranat
currr:Apply Mapping Functions in Frequent Saving
Implementations of the family of map() functions with frequent saving of the intermediate results. The contained functions let you start the evaluation of the iterations where you stopped (reading the already evaluated ones from cache), and work with the currently evaluated iterations while remaining ones are running in a background job. Parallel computing is also easier with the workers parameter.
Maintained by Marcell Granat. Last updated 7 months ago.
checkpointsparallel-computingpurrr
10.0 match 21 stars 4.02 score 7 scripts
coolbutuseless
c64vice:Interface to Binary Monitor in VICE C64 Emulator
Interface to the binary monitor in VICE - the c64 emulator.
Maintained by mikefc. Last updated 1 years ago.
11.7 match 2 stars 2.08 score 12 scripts
daroczig
AWR.Kinesis:Amazon 'Kinesis' Consumer Application for Stream Processing
Fetching data from Amazon 'Kinesis' Streams using the Java-based 'MultiLangDaemon' interacting with Amazon Web Services ('AWS') for easy stream processing from R. For more information on 'Kinesis', see <https://aws.amazon.com/kinesis>.
Maintained by Gergely Daroczi. Last updated 7 years ago.
5.1 match 4 stars 4.30 score 7 scripts
bioc
Rqc:Quality Control Tool for High-Throughput Sequencing Data
Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics.
Maintained by Welliton Souza. Last updated 5 months ago.
sequencingqualitycontroldataimportcpp
3.3 match 6.00 score 67 scripts
tbates
umx:Structural Equation Modeling and Twin Modeling in R
Quickly create, run, and report structural equation models, and twin models. See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.
Maintained by Timothy C. Bates. Last updated 5 hours ago.
behavior-geneticsgeneticsopenmxpsychologysemstatisticsstructural-equation-modelingtutorialstwin-modelsumx
1.9 match 44 stars 9.45 score 472 scripts
mlr-org
mlr3torch:Deep Learning with 'mlr3'
Deep Learning library that extends the mlr3 framework by building upon the 'torch' package. It allows to conveniently build, train, and evaluate deep learning models without having to worry about low level details. Custom architectures can be created using the graph language defined in 'mlr3pipelines'.
Maintained by Sebastian Fischer. Last updated 30 days ago.
data-sciencedeep-learningmachine-learningmlr3torch
2.3 match 42 stars 7.63 score 78 scripts
augustinewigle
poth:Precision of Treatment Hierarchy (POTH)
Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and cumulative POTH can also be calculated to improve interpretation of treatment hierarchies.
Maintained by Augustine Wigle. Last updated 5 months ago.
3.6 match 1 stars 3.65 scorebioc
CoGAPS:Coordinated Gene Activity in Pattern Sets
Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.
Maintained by Elana J. Fertig. Last updated 5 months ago.
geneexpressiontranscriptiongenesetenrichmentdifferentialexpressionbayesianclusteringtimecoursernaseqmicroarraymultiplecomparisondimensionreductionimmunooncologycpp
1.8 match 6.72 score 104 scripts
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
2.0 match 20 stars 5.20 score 16 scripts
clandere
AnaCoDa:Analysis of Codon Data under Stationarity using a Bayesian Framework
Is a collection of models to analyze genome scale codon data using a Bayesian framework. Provides visualization routines and checkpointing for model fittings. Currently published models to analyze gene data for selection on codon usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In addition 'AnaCoDa' contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data.
Maintained by Cedric Landerer. Last updated 4 years ago.
2.5 match 1 stars 4.00 score 100 scripts
matsuurakentaro
RLoptimal:Optimal Adaptive Allocation Using Deep Reinforcement Learning
An implementation to compute an optimal adaptive allocation rule using deep reinforcement learning in a dose-response study (Matsuura et al. (2022) <doi:10.1002/sim.9247>). The adaptive allocation rule can directly optimize a performance metric, such as power, accuracy of the estimated target dose, or mean absolute error over the estimated dose-response curve.
Maintained by Kentaro Matsuura. Last updated 2 months ago.
1.5 match 4 stars 5.95 score 21 scriptsmatsuurakentaro
RLescalation:Optimal Dose Escalation Using Deep Reinforcement Learning
An implementation to compute an optimal dose escalation rule using deep reinforcement learning in phase I oncology trials (Matsuura et al. (2023) <doi:10.1080/10543406.2023.2170402>). The dose escalation rule can directly optimize the percentages of correct selection (PCS) of the maximum tolerated dose (MTD).
Maintained by Kentaro Matsuura. Last updated 1 months ago.
1.5 match 4.18 score
jackdunnnz
iai:Interface to 'Interpretable AI' Modules
An interface to the algorithms of 'Interpretable AI' <https://www.interpretable.ai> from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available.
Maintained by Jack Dunn. Last updated 5 months ago.
1.9 match 1 stars 2.00 score 7 scripts
bioc
DaMiRseq:Data Mining for RNA-seq data: normalization, feature selection and classification
The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot.
Maintained by Mattia Chiesa. Last updated 5 months ago.
sequencingrnaseqclassificationimmunooncologyopenjdk
0.5 match 5.32 score 7 scripts 1 dependents