Showing 200 of total 275 results (show query)

clementcalenge

adehabitatLT:Analysis of Animal Movements

A collection of tools for the analysis of animal movements.

Maintained by Clement Calenge. Last updated 6 months ago.

34.0 match 6 stars 8.60 score 370 scripts 12 dependents

r-simmer

simmer.plot:Plotting Methods for 'simmer'

A set of plotting methods for 'simmer' trajectories and simulations.

Maintained by Iñaki Ucar. Last updated 5 months ago.

discrete-eventplotsimulationvisualization

5.8 match 10 stars 6.18 score 152 scripts

spatstat

spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Maintained by Adrian Baddeley. Last updated 9 days ago.

analysis-of-variancecluster-processconfidence-intervalscox-processdeterminantal-point-processesgibbs-processinfluenceleveragemodel-diagnosticsneyman-scottparameter-estimationpoisson-processspatial-analysisspatial-modellingspatial-point-processesstatistical-inference

3.8 match 5 stars 9.09 score 6 scripts 46 dependents

rmi-pacta

pacta.interactive.plot:What the Package Does (One Line, Title Case)

What the package does (one paragraph).

Maintained by CJ Yetman. Last updated 8 months ago.

d3data-visualizationinteractive-visualizationspactar2d3

6.6 match 2 stars 5.16 score 7 scripts 1 dependents

growthcharts

brokenstick:Broken Stick Model for Irregular Longitudinal Data

Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.

Maintained by Stef van Buuren. Last updated 2 years ago.

b-splinegrowth-curveslinear-mixed-modelslongitudinal-data

5.9 match 9 stars 5.33 score 12 scripts

r-simmer

simmer.bricks:Helper Methods for 'simmer' Trajectories

Provides wrappers for common activity patterns in 'simmer' trajectories.

Maintained by Iñaki Ucar. Last updated 2 years ago.

discrete-eventsimulation

5.4 match 6 stars 5.64 score 49 scripts 1 dependents

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

2.4 match 31 stars 11.46 score 474 scripts 25 dependents

r-forge

R2MLwiN:Running 'MLwiN' from Within R

An R command interface to the 'MLwiN' multilevel modelling software package.

Maintained by Zhengzheng Zhang. Last updated 5 months ago.

4.9 match 5.35 score 125 scripts

miguel-porto

SiMRiv:Simulating Multistate Movements in River/Heterogeneous Landscapes

Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of 'Movement Ecology' hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics.

Maintained by Miguel Porto. Last updated 6 months ago.

animal-movementheterogeneous-landscapesmovement-ecologyriver-networkssimulation

4.1 match 15 stars 6.08 score 27 scripts 1 dependents

pik-piam

rmndt:Tools for data.table objects in the REMIND context

Helper functions for REMIND-related tasks with data.table objects, e.g., interpolation and (dis-)aggregation.

Maintained by Alois Dirnaichner. Last updated 11 months ago.

7.3 match 2.95 score 10 scripts 6 dependents

yeyuan98

clockSim:Simulation of the Circadian Clock Gene Network

A preconfigured simulation workflow for the circadian clock gene network.

Maintained by Ye Yuan. Last updated 8 days ago.

7.2 match 2.78 score 3 scripts

pik-piam

mrindustry:input data generation for the REMIND industry module

The mrindustry packages contains data preprocessing for the REMIND model.

Maintained by Falk Benke. Last updated 10 hours ago.

3.6 match 5.43 score 2 dependents

craig-pt

tsgc:Time Series Methods Based on Growth Curves

The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories. It is designed to model the progression of an epidemic over time while accounting for the various uncertainties inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach, using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves. The forecasts generated by the package are of value to public health officials and researchers who need to understand and predict the course of an epidemic to inform decision-making. Beyond its application in public health, the package is also a useful resource for researchers and practitioners in fields where the trajectories of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy. The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021) <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024) <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.

Maintained by Craig Thamotheram. Last updated 7 months ago.

3.5 match 1 stars 4.86 score 24 scripts

venelin

PCMBase:Simulation and Likelihood Calculation of Phylogenetic Comparative Models

Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.

Maintained by Venelin Mitov. Last updated 10 months ago.

1.8 match 6 stars 7.56 score 85 scripts 3 dependents

pik-piam

mrremind:MadRat REMIND Input Data Package

The mrremind packages contains data preprocessing for the REMIND model.

Maintained by Lavinia Baumstark. Last updated 10 hours ago.

1.7 match 4 stars 6.26 score 15 scripts 1 dependents

usaid-oha-si

mindthegap:Mind the Gap

Package to tidy UNAIDS estimates (from the EDMS database) as well as plot trends in UNAIDS 95 goals and ART coverage gap by country.

Maintained by Karishma Srikanth. Last updated 2 months ago.

1.9 match 5 stars 5.51 score 13 scripts