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facebookexperimental
Robyn:Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science
Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.
Maintained by Gufeng Zhou. Last updated 10 days ago.
adstockingbudget-allocationcost-response-curveeconometricsevolutionary-algorithmgradient-based-optimisationhyperparameter-optimizationmarketing-mix-modelingmarketing-mix-modellingmarketing-sciencemmmridge-regression
1.3k stars 10.27 score 95 scripts
steffenmoritz
ridge:Ridge Regression with Automatic Selection of the Penalty Parameter
Linear and logistic ridge regression functions. Additionally includes special functions for genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.
Maintained by Steffen Moritz. Last updated 3 years ago.
18 stars 7.24 score 124 scripts 2 dependents
faosorios
fastmatrix:Fast Computation of some Matrices Useful in Statistics
Small set of functions to fast computation of some matrices and operations useful in statistics and econometrics. Currently, there are functions for efficient computation of duplication, commutation and symmetrizer matrices with minimal storage requirements. Some commonly used matrix decompositions (LU and LDL), basic matrix operations (for instance, Hadamard, Kronecker products and the Sherman-Morrison formula) and iterative solvers for linear systems are also available. In addition, the package includes a number of common statistical procedures such as the sweep operator, weighted mean and covariance matrix using an online algorithm, linear regression (using Cholesky, QR, SVD, sweep operator and conjugate gradients methods), ridge regression (with optimal selection of the ridge parameter considering several procedures), omnibus tests for univariate normality, functions to compute the multivariate skewness, kurtosis, the Mahalanobis distance (checking the positive defineteness), and the Wilson-Hilferty transformation of gamma variables. Furthermore, the package provides interfaces to C code callable by another C code from other R packages.
Maintained by Felipe Osorio. Last updated 1 years ago.
commutation-matrixjarque-bera-testldl-factorizationlu-factorizationmatrix-api-for-r-packagesmatrix-normsmodified-choleskyols-regressionpower-methodridge-regressionsherman-morrisonstatisticssweep-operatorsymmetrizer-matrixfortranopenblas
19 stars 6.37 score 37 scripts 11 dependents
ahaeusser
echos:Echo State Networks for Time Series Modeling and Forecasting
Provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).
Maintained by Alexander Häußer. Last updated 25 days ago.
echo-state-networksfablefabletoolsforecastforecastingrecurrent-neural-networksreservoir-computingridge-regressiontime-seriesopenblascppopenmp
12 stars 6.03 score 8 scripts
friendly
genridge:Generalized Ridge Trace Plots for Ridge Regression
The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.
Maintained by Michael Friendly. Last updated 4 months ago.
bias-variancegraphicsprincipal-component-analysisregression-modelsridge-regressionsingular-value-decomposition
4 stars 4.84 score 69 scripts