R-universe search: needs:drat

Showing 11 of total 11 results (show query)

kharchenkolab

pagoda2:Single Cell Analysis and Differential Expression

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

scrna-seq single-cell single-cell-rna-seq transcriptomics openblas cpp openmp

223 stars 8.00 score 282 scripts

syoung9836

knfi:Analysis of Korean National Forest Inventory Database

Understanding the current status of forest resources is essential for monitoring changes in forest ecosystems and generating related statistics. In South Korea, the National Forest Inventory (NFI) surveys over 4,500 sample plots nationwide every five years and records 70 items, including forest stand, forest resource, and forest vegetation surveys. Many researchers use NFI as the primary data for research, such as biomass estimation or analyzing the importance value of each species over time and space, depending on the research purpose. However, the large volume of accumulated forest survey data from across the country can make it challenging to manage and utilize such a vast dataset. To address this issue, we developed an R package that efficiently handles large-scale NFI data across time and space. The package offers a comprehensive workflow for NFI data analysis. It starts with data processing, where read_nfi() function reconstructs NFI data according to the researcher's needs while performing basic integrity checks for data quality.Following this, the package provides analytical tools that operate on the verified data. These include functions like summary_nfi() for summary statistics, diversity_nfi() for biodiversity analysis, iv_nfi() for calculating species importance value, and biomass_nfi() and cwd_biomass_nfi() for biomass estimation. Finally, for visualization, the tsvis_nfi() function generates graphs and maps, allowing users to visualize forest ecosystem changes across various spatial and temporal scales. This integrated approach and its specialized functions can enhance the efficiency of processing and analyzing NFI data, providing researchers with insights into forest ecosystems. The NFI Excel files (.xlsx) are not included in the R package and must be downloaded separately. Users can access these NFI Excel files by visiting the Korea Forest Service Forestry Statistics Platform <https://kfss.forest.go.kr/stat/ptl/article/articleList.do?curMenu=11694&bbsId=microdataboard> to download the annual NFI Excel files, which are bundled in .zip archives. Please note that this website is only available in Korean, and direct download links can be found in the notes section of the read_nfi() function.

Maintained by Sinyoung Park. Last updated 4 months ago.

data-analysis-r forestry

1 stars 4.48 score 2 scripts

ifpri

GFSir:Global Food Security analysis using IMPACT model and R

Global Food Security analysis using IMPACT model and R.

Maintained by Abhijeet Mishra. Last updated 4 days ago.

3.40 score 1 scripts

ifpri

DOORMAT:Data processor for IMPACT model (Data prOcessor fOR iMpAcT)

Data processor for IMPACT model.

Maintained by Abhijeet Mishra. Last updated 10 months ago.

3.18 score 5 dependents

mrc-ide

provisionr:Provision a Library

Provision a set of packages into a new library, possibly for use on a different architecture.

Maintained by Rich FitzJohn. Last updated 5 years ago.

cluster infrastructure

27 stars 3.13 score 1 scripts

leef-uzh

LEEF:Data Package Containing Only Data and Data Information

Setup package for the LEEF pipeline which loads / installs all necessary packages and functions to run the pipeline.

Maintained by Rainer M. Krug. Last updated 3 years ago.

data-analysis data-processing leef

2.95 score

ifpri

ARIA:App for IMPACT (App foR ImpAct)

App for IMPACT (App foR ImpAct).

Maintained by Abhijeet Mishra. Last updated 10 months ago.

2.70 score 6 scripts

ifpri

reportIMPACT:IMPACT outputs R package for IMPACT version 3.x

Common output routines for extracting results from the IMPACT framework (versions 3.x).

Maintained by Abhijeet Mishra. Last updated 1 years ago.

2.18 score 1 dependents

zeee1

tashu:Analysis and Prediction of Bicycle Rental Amount

Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions. Functions on this package interacts with data on 'tashudata' package, a 'drat' repository. 'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. To install this data package, see the instructions at <https://github.com/zeee1/Tashu_Rpackage>. top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths. daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental. create_train_dataset(), create_test_dataset() is data processing function for prediction. Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset. Users can make random-forest prediction model by using create_train_model() and predict amount of bicycle rental in 2015 by using predict_bike_rental().

Maintained by Jiwon Min. Last updated 4 years ago.

2.00 score 2 scripts

ifpri

EFFORT:Emission Factors For IMPACT

Emission Factors FOR impacT.

Maintained by Abhijeet Mishra. Last updated 12 months ago.

1.30 score

ifpri

STATEROOM:SpaTiAl resulTs gEneRatOr fOr iMpact (STATEROOM)

Spatial results generator for IMPACT.

Maintained by Abhijeet Mishra. Last updated 2 years ago.

1.00 score