Showing 10 of total 10 results (show query)
tidymodels
parsnip:A Common API to Modeling and Analysis Functions
A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', 'H2O', etc).
Maintained by Max Kuhn. Last updated 18 days ago.
612 stars 16.37 score 3.4k scripts 69 dependents
config-i1
smooth:Forecasting Using State Space Models
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>), SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).
Maintained by Ivan Svetunkov. Last updated 13 days ago.
arimaarima-forecastingcesetsexponential-smoothingforecaststate-spacetime-seriesopenblascpp
90 stars 13.83 score 412 scripts 25 dependents
bioc
mixOmics:Omics Data Integration Project
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
Maintained by Eva Hamrud. Last updated 2 days ago.
immunooncologymicroarraysequencingmetabolomicsmetagenomicsproteomicsgenepredictionmultiplecomparisonclassificationregressionbioconductorgenomicsgenomics-datagenomics-visualizationmultivariate-analysismultivariate-statisticsomicsr-pkgr-project
185 stars 13.75 score 1.3k scripts 22 dependents
pilaboratory
sads:Maximum Likelihood Models for Species Abundance Distributions
Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.
Maintained by Paulo I. Prado. Last updated 1 years ago.
23 stars 8.66 score 244 scripts 3 dependents
svkucheryavski
mdatools:Multivariate Data Analysis for Chemometrics
Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.
Maintained by Sergey Kucheryavskiy. Last updated 8 months ago.
36 stars 7.41 score 220 scripts 1 dependents
taddylab
textir:Inverse Regression for Text Analysis
Multinomial (inverse) regression inference for text documents and associated attributes. For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included. Note that the topic modeling capability of earlier 'textir' is now a separate package, 'maptpx'.
Maintained by Matt Taddy. Last updated 7 years ago.
29 stars 6.03 score 123 scripts 2 dependentsbioc
autonomics:Unified Statistical Modeling of Omics Data
This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.
Maintained by Aditya Bhagwat. Last updated 2 months ago.
softwaredataimportpreprocessingdimensionreductionprincipalcomponentregressiondifferentialexpressiongenesetenrichmenttranscriptomicstranscriptiongeneexpressionrnaseqmicroarrayproteomicsmetabolomicsmassspectrometry
5.89 score 5 scripts
khliland
ER:Effect + Residual Modelling
Multivariate modeling of data after deflation of interfering effects. EF Mosleth et al. (2021) <doi:10.1038/s41598-021-82388-w> and EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>.
Maintained by Kristian Hovde Liland. Last updated 2 years ago.
3.00 score 1 scripts
cran
cols:Constrained Ordinary Least Squares
Constrained ordinary least squares is performed. One constraint is that all beta coefficients (including the constant) cannot be negative. They can be either 0 or strictly positive. Another constraint is that the sum of the beta coefficients equals a constant. References: Hansen, B. E. (2022). Econometrics, Princeton University Press. <ISBN:9780691235899>.
Maintained by Michail Tsagris. Last updated 3 months ago.
1.48 scorecran
fastPLS:A Fast Implementation of Partial Least Square
An implementation in 'Rcpp' / 'RcppArmadillo' of Partial Least Square algorithms. This package includes other functions to perform the double cross-validation and a fast correlation.
Maintained by Stefano Cacciatore. Last updated 4 months ago.
1.00 score