Showing 9 of total 9 results (show query)
hwborchers
pracma:Practical Numerical Math Functions
Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.
Maintained by Hans W. Borchers. Last updated 1 years ago.
29 stars 12.34 score 6.6k scripts 931 dependents
pbreheny
ncvreg:Regularization Paths for SCAD and MCP Penalized Regression Models
Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided. For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388> or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.
Maintained by Patrick Breheny. Last updated 12 days ago.
43 stars 12.03 score 458 scripts 38 dependents
bioxgeo
geodiv:Methods for Calculating Gradient Surface Metrics
Methods for calculating gradient surface metrics for continuous analysis of landscape features.
Maintained by Annie C. Smith. Last updated 1 years ago.
11 stars 5.88 score 23 scripts 1 dependents
longhaisk
HTLR:Bayesian Logistic Regression with Heavy-Tailed Priors
Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.
Maintained by Longhai Li. Last updated 5 months ago.
bayesianclassificationhigh-dimensional-datamachine-learningmcmcopenblascppopenmp
10 stars 5.18 score 7 scripts
cran
matlab:'MATLAB' Emulation Package
Emulate 'MATLAB' code using 'R'.
Maintained by P. Roebuck. Last updated 9 months ago.
4.09 score 19 dependents
gallegoj
tfarima:Transfer Function and ARIMA Models
Building customized transfer function and ARIMA models with multiple operators and parameter restrictions. Functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment. See Bell and Hillmer (1983) <doi:10.1080/01621459.1983.10478005>, Box, Jenkins, Reinsel and Ljung <ISBN:978-1-118-67502-1>, Box, Pierce and Newbold (1987) <doi:10.1080/01621459.1987.10478430>, Box and Tiao (1975) <doi:10.1080/01621459.1975.10480264>, Chen and Liu (1993) <doi:10.1080/01621459.1993.10594321>.
Maintained by Jose L. Gallego. Last updated 1 years ago.
2 stars 4.04 score 11 scripts
matrix-profile-foundation
matrixprofiler:Matrix Profile for R
This is the core functions needed by the 'tsmp' package. The low level and carefully checked mathematical functions are here. These are implementations of the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Maintained by Francisco Bischoff. Last updated 3 years ago.
algorithmmatrix-profilercpptime-seriescpp
10 stars 3.70 score 2 scriptscran
sievetest:Laboratory Sieve Test Reporting Functions
Functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.
Maintained by Petr Matousu. Last updated 7 years ago.
1.00 score
ainsuotain
matrixProfile:Matrix Profile
A simple and the early stage package for matrix profile based on the paper of Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh (2016) <DOI:10.1109/ICDM.2016.0179>. This package calculates all-pairs-similarity for a given window size for time series data.
Maintained by Donghwan Kim. Last updated 7 years ago.
1.00 score 8 scripts