Showing 200 of total 208 results (show query)

traversc

stringfish:Alt String Implementation

Provides an extendable, performant and multithreaded 'alt-string' implementation backed by 'C++' vectors and strings.

Maintained by Travers Ching. Last updated 5 months ago.

pcre2cpp

6.0 match 67 stars 10.14 score 14 scripts 57 dependents

emptyfield-ds

emptyfield:Install Empty Field Learning Materials

Install learning materials from Empty Field Data Science.

Maintained by Malcolm Barrett. Last updated 3 years ago.

7.4 match 1 stars 2.65 score 1 scripts 3 dependents

bioc

systemPipeR:systemPipeR: Workflow Environment for Data Analysis and Report Generation

systemPipeR is a multipurpose data analysis workflow environment that unifies R with command-line tools. It enables scientists to analyze many types of large- or small-scale data on local or distributed computer systems with a high level of reproducibility, scalability and portability. At its core is a command-line interface (CLI) that adopts the Common Workflow Language (CWL). This design allows users to choose for each analysis step the optimal R or command-line software. It supports both end-to-end and partial execution of workflows with built-in restart functionalities. Efficient management of complex analysis tasks is accomplished by a flexible workflow control container class. Handling of large numbers of input samples and experimental designs is facilitated by consistent sample annotation mechanisms. As a multi-purpose workflow toolkit, systemPipeR enables users to run existing workflows, customize them or design entirely new ones while taking advantage of widely adopted data structures within the Bioconductor ecosystem. Another important core functionality is the generation of reproducible scientific analysis and technical reports. For result interpretation, systemPipeR offers a wide range of plotting functionality, while an associated Shiny App offers many useful functionalities for interactive result exploration. The vignettes linked from this page include (1) a general introduction, (2) a description of technical details, and (3) a collection of workflow templates.

Maintained by Thomas Girke. Last updated 5 months ago.

geneticsinfrastructuredataimportsequencingrnaseqriboseqchipseqmethylseqsnpgeneexpressioncoveragegenesetenrichmentalignmentqualitycontrolimmunooncologyreportwritingworkflowstepworkflowmanagement

1.5 match 53 stars 11.52 score 344 scripts 3 dependents

emptyfield-ds

quarto.workshop:Install Materials for Reproducible Research in R with Quarto

Install learning materials for Reproducible Research in R with Quarto.

Maintained by Malcolm Barrett. Last updated 2 years ago.

7.3 match 4 stars 2.30 score

ropensci

weatherOz:An API Client for Australian Weather and Climate Data Resources

Provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development ('DPIRD') of Western Australia and by the Science and Technology Division of the Queensland Government's Department of Environment and Science ('DES'). As well as the Bureau of Meteorology ('BOM') of the Australian government precis and coastal forecasts, and downloading and importing radar and satellite imagery files. 'DPIRD' weather data are accessed through public 'APIs' provided by 'DPIRD', <https://www.agric.wa.gov.au/weather-api-20>, providing access to weather station data from the 'DPIRD' weather station network. Australia-wide weather data are based on data from the Australian Bureau of Meteorology ('BOM') data and accessed through 'SILO' (Scientific Information for Land Owners) Jeffrey et al. (2001) <doi:10.1016/S1364-8152(01)00008-1>. 'DPIRD' data are made available under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU) license <https://creativecommons.org/licenses/by/3.0/au/deed.en>. SILO data are released under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) <https://creativecommons.org/licenses/by/4.0/>. 'BOM' data are (c) Australian Government Bureau of Meteorology and released under a Creative Commons (CC) Attribution 3.0 licence or Public Access Licence ('PAL') as appropriate, see <http://www.bom.gov.au/other/copyright.shtml> for further details.

Maintained by Rodrigo Pires. Last updated 1 months ago.

dpirdbommeteorological-dataweather-forecastaustraliaweatherweather-datameteorologywestern-australiaaustralia-bureau-of-meteorologywestern-australia-agricultureaustralia-agricultureaustralia-climateaustralia-weatherapi-clientclimatedatarainfallweather-api

1.5 match 31 stars 8.47 score 40 scripts

emptyfield-ds

rrr.workshop:Install Materials for Reproducible Research in R

Install learning materials for Reproducible Research in R.

Maintained by Malcolm Barrett. Last updated 4 years ago.

7.4 match 1.70 score 1 scripts

emptyfield-ds

rpkg.workshop:Install Materials for Developing R Packages

Install learning materials for Developing R Packages.

Maintained by Malcolm Barrett. Last updated 3 years ago.

7.4 match 1.70 score 1 scripts

gaborcsardi

sankey:Illustrate the Flow of Information or Material

Plots that illustrate the flow of information or material.

Maintained by Gábor Csárdi. Last updated 7 years ago.

3.7 match 3 stars 3.29 score 13 scripts

peterkdunn

GLMsData:Generalized Linear Model Data Sets

Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.

Maintained by Peter K. Dunn. Last updated 3 years ago.

4.0 match 2.53 score 220 scripts

pik-piam

mrland:MadRaT land data package

The package provides land related data via the madrat framework.

Maintained by Jan Philipp Dietrich. Last updated 12 days ago.

1.8 match 5.59 score 3 scripts 4 dependents

doserjef

rFIA:Estimation of Forest Variables using the FIA Database

The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.

Maintained by Jeffrey Doser. Last updated 24 days ago.

compute-estimatesfiafia-databasefia-datamartforest-inventoryforest-variablesinventoriesspace-timespatial

1.7 match 49 stars 5.93 score

mikejohnson51

AOI:Areas of Interest

A consistent tool kit for forward and reverse geocoding and defining boundaries for spatial analysis.

Maintained by Mike Johnson. Last updated 1 years ago.

aoiarea-of-interestbounding-boxesgisspatialsubset

1.9 match 37 stars 4.98 score 174 scripts 1 dependents

merliseclyde

BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Maintained by Merlise Clyde. Last updated 4 months ago.

bayesianbayesian-inferencegeneralized-linear-modelslinear-regressionlogistic-regressionmcmcmodel-selectionpoisson-regressionpredictive-modelingregressionvariable-selectionfortranopenblas

0.8 match 44 stars 10.63 score 420 scripts 3 dependents

emptyfield-ds

shunyata:Deploy Empty Field Teaching Material

This package helps set up new teaching material and deploy courses based on the Empty Field teaching workflow.

Maintained by Malcolm Barrett. Last updated 1 years ago.

3.5 match 1 stars 1.70 score 2 scripts

cran

mdscore:Improved Score Tests for Generalized Linear Models

A set of functions to obtain modified score test for generalized linear models.

Maintained by Antonio Hermes M. da Silva-Junior. Last updated 8 years ago.

3.8 match 1.00 score

meganmorbitzer

ddiv:Data Driven I-v Feature Extraction

The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be found in the following paper. [1] Ma, X. et al, 2019. <doi:10.1109/JPHOTOV.2019.2928477>.

Maintained by Megan M. Morbitzer. Last updated 4 years ago.

0.5 match 3.62 score 23 scripts 1 dependents

rdinnager

sdmpack:FIU SDM Course Package

Course material for FIU course on SDM

Maintained by Russell Dinnage. Last updated 1 years ago.

0.6 match 2.08 score 24 scripts