Showing 10 of total 10 results (show query)
alsabtay
ATAforecasting:Automatic Time Series Analysis and Forecasting using the Ata Method
The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.
Maintained by Ali Sabri Taylan. Last updated 2 years ago.
ataataforecastingfableforecastforecastingtime-seriescpp
56.3 match 5 stars 3.88 score 4 scripts 1 dependentsalsabtay
fable.ata:'ATAforecasting' Modelling Interface for 'fable' Framework
Allows ATA (Automatic Time series analysis using the Ata method) models from the 'ATAforecasting' package to be used in a tidy workflow with the modeling interface of 'fabletools'. This extends 'ATAforecasting' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).
Maintained by Ali Sabri Taylan. Last updated 2 years ago.
ataforecastingfablefabletoolsforecastforecasting
53.4 match 4 stars 3.30 score 9 scripts
chajewski
ata:Automated Test Assembly
Provides a collection of psychometric methods to process item metadata and use target assessment and measurement blueprint constraints to assemble a test form. Currently two automatic test assembly (ata) approaches are enabled. For example, the weighted (positive) deviations method, wdm(), proposed by Swanson and Stocking (1993) <doi:10.1177/014662169301700205> was implemented in its full specification allowing for both item selection as well as test form refinement. The linear constraint programming approach, atalp(), uses the linear equation solver by Berkelaar et. al (2014) <http://lpsolve.sourceforge.net/5.5/> to enable a variety of approaches to select items.
Maintained by Michael Chajewski. Last updated 4 years ago.
71.3 match 1.00 score
mages
ChainLadder:Statistical Methods and Models for Claims Reserving in General Insurance
Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.
Maintained by Markus Gesmann. Last updated 1 months ago.
5.1 match 82 stars 10.04 score 196 scripts 2 dependents
cran
vipor:Plot Categorical Data Using Quasirandom Noise and Density Estimates
Generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise.
Maintained by Scott Sherrill-Mix. Last updated 1 years ago.
3.8 match 6.86 score 95 dependents
xluo11
xxIRT:Item Response Theory and Computer-Based Testing
A suite of psychometric analysis tools for research and operation, including: (1) computation of probability, information, and likelihood for the 3PL, GPCM, and GRM; (2) parameter estimation using joint or marginal likelihood estimation method; (3) simulation of computerized adaptive testing using built-in or customized algorithms; (4) assembly and simulation of multistage testing. The full documentation and tutorials are at <https://github.com/xluo11/xxIRT>.
Maintained by Xiao Luo. Last updated 6 years ago.
5.2 match 25 stars 4.10 score 10 scriptsxluo11
Rata:Automated Test Assembly
Automated test assembly of linear and adaptive tests using the mixed-integer programming. The full documentation and tutorials are at <https://github.com/xluo11/Rata>.
Maintained by Xiao Luo. Last updated 5 years ago.
5.2 match 3 stars 3.65 score 1 dependents
guokai8
o2plsda:Multiomics Data Integration
Provides functions to do 'O2PLS-DA' analysis for multiple omics data integration. The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" which published by Johan Trygg and Svante Wold at 2003 <doi:10.1002/cem.775>. 'O2PLS' is a bidirectional multivariate regression method that aims to separate the covariance between two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 <doi:10.1002/cem.1388>; Löfstedt et al., 2012 <doi:10.1016/j.aca.2013.06.026>) from the systematic sources of variance being specific for each data set separately.
Maintained by Kai Guo. Last updated 27 days ago.
integrationmulti-omicso2plsomicsplsdaopenblascppopenmp
3.3 match 6 stars 4.95 score 6 scripts
beckerbenj
eatATA:Create Constraints for Small Test Assembly Problems
Provides simple functions to create constraints for small test assembly problems (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, 'GLPK', 'lpSolve', 'Symphony', and 'Gurobi' are supported as solvers. The 'gurobi' package is not available from any mainstream repository; see <https://www.gurobi.com/downloads/>.
Maintained by Benjamin Becker. Last updated 2 months ago.
1.5 match 4 stars 5.68 score 20 scriptscran
PEIMAN2:Post-Translational Modification Enrichment, Integration, and Matching Analysis
Functions and mined database from 'UniProt' focusing on post-translational modifications to do single enrichment analysis (SEA) and protein set enrichment analysis (PSEA). Payman Nickchi, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2022) <bioRxiv:10.1101/2022.11.09.515610>.
Maintained by Payman Nickchi. Last updated 2 years ago.
0.5 match 2.70 score