close

Showing 15 of total 15 results (show query)

r-forge

surveillance:Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

Maintained by Sebastian Meyer. Last updated 2 days ago.

cpp

2 stars 10.65 score 446 scripts 3 dependents

flr

FLCore:Core Package of FLR, Fisheries Modelling in R

Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.

Maintained by Iago Mosqueira. Last updated 10 days ago.

fisheriesflrfisheries-modelling

16 stars 8.78 score 956 scripts 23 dependents

cran

boot:Bootstrap Functions (Originally by Angelo Canty for S)

Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.

Maintained by Alessandra R. Brazzale. Last updated 7 months ago.

2 stars 8.21 score 2.3k dependents

flr

mse:Tools for Running Management Strategy Evaluations using FLR

A set of functions and methods to enable the development and running of Management Strategy Evaluation (MSE) analyses, using the FLR packages and classes and the a4a methods and algorithms.

Maintained by Iago Mosqueira. Last updated 1 months ago.

simulationmsefisheriesflra4a

4 stars 6.99 score 137 scripts 3 dependents

bioc

deepSNV:Detection of subclonal SNVs in deep sequencing data.

This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC.

Maintained by Moritz Gerstung. Last updated 5 months ago.

geneticvariabilitysnpsequencinggeneticsdataimportcurlbzip2xz-utilszlibcpp

6.53 score 38 scripts 1 dependents

bioc

Repitools:Epigenomic tools

Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc.

Maintained by Mark Robinson. Last updated 5 months ago.

dnamethylationgeneexpressionmethylseq

5.90 score 267 scripts

bioc

MLSeq:Machine Learning Interface for RNA-Seq Data

This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data.

Maintained by Gokmen Zararsiz. Last updated 5 months ago.

immunooncologysequencingrnaseqclassificationclustering

4.81 score 27 scripts 1 dependents

cran

speedglm:Fitting Linear and Generalized Linear Models to Large Data Sets

Fitting linear models and generalized linear models to large data sets by updating algorithms, according to the method described in Enea (2009, ISBN: 9788861294257).

Maintained by Marco Enea. Last updated 2 years ago.

9 stars 4.65 score 12 dependents

pepijn-devries

openmpt:Open 'ModPlug' Tracker Port

Tracker music uses audio samples and pattern tables to organise musical compositions. Such music is stored in module files. This package reads, renders and plays module files using the 'libopenmpt' library <https://lib.openmpt.org/>.

Maintained by Pepijn de Vries. Last updated 2 months ago.

audiomoduleslibopenmptportaudio19cpp

2 stars 4.43 score 7 scripts

cropmodels

Recocrop:Estimating Environmental Suitability for Plants

The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).

Maintained by Robert J. Hijmans. Last updated 3 years ago.

cpp

11 stars 3.82 score 12 scripts

dncr

NBLDA:Negative Binomial Linear Discriminant Analysis

We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version.

Maintained by Dincer Goksuluk. Last updated 3 years ago.

3.54 score 2 scripts

bioc

BasicSTARRseq:Basic peak calling on STARR-seq data

Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17.

Maintained by Annika Buerger. Last updated 5 months ago.

peakdetectiongeneregulationfunctionalpredictionfunctionalgenomicscoverage

3.30 score 1 scripts

flr

AAP:Aarts and Poos Stock Assessment Model that Estimates Bycatch

FLR version of Aarts and Poos stock assessment model.

Maintained by Iago Mosqueira. Last updated 2 years ago.

2.70 score 5 scripts

cran

mosqcontrol:Mosquito Control Resource Optimization

This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments.

Maintained by Travis Byrum. Last updated 5 years ago.

2.70 score

michaelfop

covglasso:Sparse Covariance Matrix Estimation

Direct sparse covariance matrix estimation via the covariance graphical lasso by Bien, Tibshirani (2011) <doi:10.1093/biomet/asr054> using the fast coordinate descent algorithm of Wang (2014) <doi:10.1007/s11222-013-9385-5>.

Maintained by Michael Fop. Last updated 4 years ago.

openblascpp

1.48 score 1 scripts 1 dependents

: