Showing 15 of total 15 results (show query)
tidymodels
dials:Tools for Creating Tuning Parameter Values
Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.
Maintained by Hannah Frick. Last updated 1 months ago.
114 stars 14.31 score 426 scripts 52 dependents
flr
FLCore:Core Package of FLR, Fisheries Modelling in R
Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.
Maintained by Iago Mosqueira. Last updated 10 days ago.
fisheriesflrfisheries-modelling
16 stars 8.78 score 956 scripts 23 dependents
stephenslab
fastTopics:Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data
Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.
Maintained by Peter Carbonetto. Last updated 1 months ago.
79 stars 8.38 score 678 scripts 1 dependents
tpetzoldt
growthrates:Estimate Growth Rates from Experimental Data
A collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.
Maintained by Thomas Petzoldt. Last updated 2 years ago.
27 stars 7.52 score 102 scriptsflr
FLBRP:Reference Points for Fisheries Management
Calculates a range of biological reference points based upon yield per recruit and stock recruit based equilibrium calculations. These include F based reference points like F0.1, FMSY and biomass based reference points like BMSY.
Maintained by Iago Mosqueira. Last updated 4 months ago.
reference pointsfisheriesflrcpp
2 stars 6.58 score 350 scripts 4 dependents
syksy
oscar:Optimal Subset Cardinality Regression (OSCAR) Models Using the L0-Pseudonorm
Optimal Subset Cardinality Regression (OSCAR) models offer regularized linear regression using the L0-pseudonorm, conventionally known as the number of non-zero coefficients. The package estimates an optimal subset of features using the L0-penalization via cross-validation, bootstrapping and visual diagnostics. Effective Fortran implementations are offered along the package for finding optima for the DC-decomposition, which is used for transforming the discrete L0-regularized optimization problem into a continuous non-convex optimization task. These optimization modules include DBDC ('Double Bundle method for nonsmooth DC optimization' as described in Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited Memory Bundle Method for large-scale nonsmooth optimization' as in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The OSCAR models are comprehensively exemplified in Halkola et al. (2023) <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families are supported: Cox, logistic, and Gaussian.
Maintained by Teemu Daniel Laajala. Last updated 1 years ago.
fortranpenalized-regressionregression
2 stars 4.34 score 11 scriptsflr
FLFishery:Classes and Methods for Simpler Fleet/Fishery Modelling
A set of classes and methods for modelling of fleet dynamics. Fisheries are groups of vessels sharing an effort time series, with static or changing spatio-temporal patterns in selectivity.
Maintained by Iago Mosqueira. Last updated 4 months ago.
3.02 score 8 scripts 7 dependents
gtromano
DeCAFS:Detecting Changes in Autocorrelated and Fluctuating Signals
Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2021) <doi:10.1080/01621459.2021.1909598>.
Maintained by Gaetano Romano. Last updated 2 years ago.
change-detectionchangepoint-detectiontime-series-analysiscpp
2 stars 3.00 score 2 scripts
s1107967177
dynetNLAResistance:Resisting Neighbor Label Attack in a Dynamic Network
An anonymization algorithm to resist neighbor label attack in a dynamic network.
Maintained by Jiaqi Tang. Last updated 8 years ago.
2.70 score 4 scripts
yinxy1992
M2SMF:Multi-Modal Similarity Matrix Factorization for Integrative Multi-Omics Data Analysis
A new method to implement clustering from multiple modality data of certain samples, the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data.
Maintained by Xiaoyao Yin. Last updated 5 years ago.
2.23 score 17 scripts
jaderlugon
gripp:General Inverse Problem Platform
Set of functions designed to solve inverse problems. The direct problem is used to calculate a cost function to be minimized. Here are listed some papers using Inverse Problems solvers and sensitivity analysis: (Jader Lugon Jr.; Antonio J. Silva Neto 2011) <doi:10.1590/S1678-58782011000400003>. (Jader Lugon Jr.; Antonio J. Silva Neto; Pedro P.G.W. Rodrigues 2008) <doi:10.1080/17415970802082864>. (Jader Lugon Jr.; Antonio J. Silva Neto; Cesar C. Santana 2008) <doi:10.1080/17415970802082922>.
Maintained by Jader Lugon Junior. Last updated 6 years ago.
2.00 score 2 scriptsyinxy1992
OSNMTF:Orthogonal Sparse Non-Negative Matrix Tri-Factorization
A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC.
Maintained by Xiaoyao Yin. Last updated 5 years ago.
2.00 score 2 scriptsyinxy1992
M2SMJF:Multi-Modal Similarity Matrix Joint Factorization
A new method to implement clustering from multiple modality data of certain samples, the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data.
Maintained by Xiaoyao Yin. Last updated 4 years ago.
2.00 score 2 scripts
cran
cpop:Detection of Multiple Changes in Slope in Univariate Time-Series
Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) <doi:10.1080/10618600.2018.1512868>. This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope. Further information regarding the use of this package with detailed examples can be found in Fearnhead and Grose (2024) <doi:10.18637/jss.v109.i07>.
Maintained by Daniel Grose. Last updated 10 months ago.
1.00 score