LogConcDEAD - Log-Concave Density Estimation in Arbitrary Dimensions

Software for computing a log-concave (maximum likelihood) estimator for independent and identically distributed data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).

Last updated 5 months ago

openblas

3.14 score 1 dependents 23 scripts 626 downloads

breakfast - Methods for Fast Multiple Change-Point/Break-Point Detection and Estimation

A developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences.

Last updated 4 months ago

cpp

2.87 score 1 stars 37 scripts 414 downloads

scar - Shape-Constrained Additive Regression: a Maximum Likelihood Approach

Computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing. For details, see Chen and Samworth (2016) <doi:10.1111/rssb.12137>.

Last updated 3 years ago

1.78 score 1 dependents 20 scripts 247 downloads

not - Narrowest-Over-Threshold Change-Point Detection

Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) <doi:10.1111/rssb.12322>.

Last updated 4 months ago

openmp

1.76 score 58 scripts 353 downloads

Sshaped - Nonparametric, Tuning-Free Estimation of S-Shaped Functions

Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2022) <doi:10.1111/rssb.12481>.

Last updated 2 years ago

openblascpp

1.00 score 203 downloads