Showing 200 of total 249 results (show query)

miraisolutions

XLConnect:Excel Connector for R

Provides comprehensive functionality to read, write and format Excel data.

Maintained by Martin Studer. Last updated 29 days ago.

cross-platformexcelr-languagexlconnectopenjdk

130 stars 12.28 score 1.2k scripts 1 dependents

rpremrajgit

mailR:A Utility to Send Emails from R

Interface to Apache Commons Email to send emails from R.

Maintained by Rahul Premraj. Last updated 3 years ago.

openjdk

14 stars 7.46 score 374 scripts

kornl

gMCP:Graph Based Multiple Comparison Procedures

Functions and a graphical user interface for graphical described multiple test procedures.

Maintained by Kornelius Rohmeyer. Last updated 1 years ago.

openjdk

10 stars 7.31 score 105 scripts 2 dependents

s-u

venneuler:Venn and Euler Diagrams

Calculates and displays Venn and Euler Diagrams.

Maintained by Simon Urbanek. Last updated 1 years ago.

openjdk

4 stars 6.54 score 273 scripts 4 dependents

thiyangt

denguedatahub:A Tidy Format Datasets of Dengue by Country

Provides a weekly, monthly, yearly summary of dengue cases by state/ province/ country.

Maintained by Thiyanga S. Talagala. Last updated 1 months ago.

openjdk

11 stars 5.12 score 34 scripts

socialresearchcentre

dialrjars:Required 'libphonenumber' jars for the 'dialr' Package

Collects 'libphonenumber' jars required for the 'dialr' package.

Maintained by Danny Smith. Last updated 23 days ago.

openjdk

2 stars 4.93 score 4 scripts 1 dependents

bioc

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

jatanrt

eprscope:Processing and Analysis of Electron Paramagnetic Resonance Data and Spectra in Chemistry

Processing, analysis and plottting of Electron Paramagnetic Resonance (EPR) spectra in chemistry. Even though the package is mainly focused on continuous wave (CW) EPR/ENDOR, many functions may be also used for the integrated forms of 1D PULSED EPR spectra. It is able to find the most important spectral characteristics like g-factor, linewidth, maximum of derivative or integral intensities and single/double integrals. This is especially important in spectral (time) series consisting of many EPR spectra like during variable temperature experiments, electrochemical or photochemical radical generation and/or decay. Package also enables processing of data/spectra for the analytical (quantitative) purposes. Namely, how many radicals or paramagnetic centers can be found in the analyte/sample. The goal is to evaluate rate constants, considering different kinetic models, to describe the radical reactions. The key feature of the package resides in processing of the universal ASCII text formats (such as '.txt', '.csv' or '.asc') from scratch. No proprietary formats are used (except the MATLAB EasySpin outputs) and in such respect the package is in accordance with the FAIR data principles. Upon 'reading' (also providing automatic procedures for the most common EPR spectrometers) the spectral data are transformed into the universal R 'data frame' format. Subsequently, the EPR spectra can be visualized and are fully consistent either with the 'ggplot2' package or with the interactive formats based on 'plotly'. Additionally, simulations and fitting of the isotropic EPR spectra are also included in the package. Advanced simulation parameters provided by the MATLAB-EasySpin toolbox and results from the quantum chemical calculations like g-factor and hyperfine splitting/coupling constants (a/A) can be compared and summarized in table-format in order to analyze the EPR spectra by the most effective way.

Maintained by Ján Tarábek. Last updated 16 hours ago.

chemistrydata-analysisdata-visualizationepresrfittingoptimizationprogramming-languagereproducible-researchscientific-plottingspectroscopyopenjdk

4.76 score 7 scripts

r-forge

stops:Structure Optimized Proximity Scaling

Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices.

Maintained by Thomas Rusch. Last updated 3 months ago.

openjdk

1 stars 4.48 score 23 scripts

weiguonimh

mMARCH.AC:Processing of Accelerometry Data with 'GGIR' in mMARCH

Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package 'GGIR' is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with 'GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations. To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through 'GGIR' as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In module 1, all csv files in the 'GGIR' output directory were read, transformed and then merged. In module 2, the 'GGIR' output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44.

Maintained by Wei Guo. Last updated 2 years ago.

openjdk

2 stars 4.41 score 26 scripts

kapelner

GreedyExperimentalDesign:Greedy Experimental Design Construction

Computes experimental designs for a two-arm experiment with covariates via a number of methods: (0) complete randomization and randomization with forced-balance, (1) Greedily optimizing a balance objective function via pairwise switching. This optimization provides lower variance for the treatment effect estimator (and higher power) while preserving a design that is close to complete randomization. We return all iterations of the designs for use in a permutation test, (2) The second is via numerical optimization (via 'gurobi' which must be installed, see <https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>) a la Bertsimas and Kallus, (3) rerandomization, (4) Karp's method for one covariate, (5) exhaustive enumeration to find the optimal solution (only for small sample sizes), (6) Binary pair matching using the 'nbpMatching' library, (7) Binary pair matching plus design number (1) to further optimize balance, (8) Binary pair matching plus design number (3) to further optimize balance, (9) Hadamard designs, (10) Simultaneous Multiple Kernels. In (1-9) we allow for three objective functions: Mahalanobis distance, Sum of absolute differences standardized and Kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) <arXiv:1612.02315>, Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization" (2021) <arXiv:2012.03330>.

Maintained by Adam Kapelner. Last updated 24 days ago.

cppopenjdk

4.16 score 16 scripts 1 dependents

kurthornik

RWekajars:R/Weka Interface Jars

External jars required for package 'RWeka'.

Maintained by Kurt Hornik. Last updated 5 years ago.

openjdk

3.89 score 32 scripts 15 dependents

alexfoxfox

rChoiceDialogs:'rChoiceDialogs' Collection

Collection of portable choice dialog widgets.

Maintained by Alex Lisovich. Last updated 3 years ago.

openjdk

3.64 score 49 scripts 3 dependents

dmkaplan2000

RH2:DBI/RJDBC Interface to H2 Database

DBI/RJDBC interface to h2 database. h2 version 1.3.175 is included.

Maintained by "David M. Kaplan". Last updated 7 years ago.

openjdk

1 stars 3.46 score 40 scripts

kurthornik

openNLPdata:Apache OpenNLP Jars and Basic English Language Models

Apache OpenNLP jars and basic English language models.

Maintained by Kurt Hornik. Last updated 1 years ago.

openjdk

3.44 score 40 scripts 9 dependents

larskotthoff

llama:Leveraging Learning to Automatically Manage Algorithms

Provides functionality to train and evaluate algorithm selection models for portfolios.

Maintained by Lars Kotthoff. Last updated 4 years ago.

openjdk

4 stars 2.80 score 53 scripts 1 dependents

etendard7

ELT:Experience Life Tables

Build experience life tables.

Maintained by Wassim Youssef. Last updated 2 years ago.

openjdk

2.40 score 25 scripts