Showing 7 of total 7 results (show query)
feiyoung
GFM:Generalized Factor Model
Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables. Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model, respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. This model can be employed in social and behavioral sciences, economy and finance, and genomics, to extract interpretable nonlinear factors. More details can be referred to Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2021) <doi:10.1080/01621459.2021.1999818>.
Maintained by Wei Liu. Last updated 6 months ago.
approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factorsopenblascpp
2 stars 5.68 score 8 scripts 2 dependents
awamaeva
trajmsm:Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories
Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.
Maintained by Awa Diop. Last updated 1 years ago.
g-computationinverse-probability-weightsmarginal-structural-modelstmletrajectory-analysis
5 stars 3.40 score
cran
scR:Estimate Vapnik-Chervonenkis Dimension and Sample Complexity
We provide a suite of tools for estimating the sample complexity of a chosen model through theoretical bounds and simulation. The package incorporates methods for estimating the Vapnik-Chervonenkis dimension (VCD) of a chosen algorithm, which can be used to estimate its sample complexity. Alternatively, we provide simulation methods to estimate sample complexity directly. For more details, see Carter, P & Choi, D (2024). "Learning from Noise: Applying Sample Complexity for Political Science Research" <doi:10.31219/osf.io/evrcj>.
Maintained by Perry Carter. Last updated 3 months ago.
2.18 score
trevorhastie
sparsenet:Fit Sparse Linear Regression Models via Nonconvex Optimization
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Maintained by Trevor Hastie. Last updated 4 months ago.
2 stars 2.08 score 20 scripts 1 dependents
jiaqihu2021
GrFA:Group Factor Analysis
Several group factor analysis algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008> , Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and Aggregated projection method.
Maintained by Jiaqi Hu. Last updated 3 months ago.
1.30 scorejiaqihu2021
REFA:Robust Exponential Factor Analysis
A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance.
Maintained by Jiaqi Hu. Last updated 1 years ago.
1.00 score
hinohide
ider:Various Methods for Estimating Intrinsic Dimension
An implementation of various methods for estimating intrinsic dimension of vector-valued dataset or distance matrix. Most methods implemented are based on different notion of fractal dimension such as the capacity dimension, the box-counting dimension, and the information dimension.
Maintained by Hideitsu Hino. Last updated 2 years ago.
1.00 score 10 scripts