Showing 95 of total 95 results (show query)

adeckmyn

maps:Draw Geographical Maps

Display of maps. Projection code and larger maps are in separate packages ('mapproj' and 'mapdata').

Maintained by Alex Deckmyn. Last updated 2 months ago.

4.5 match 24 stars 14.70 score 19k scripts 490 dependents

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.

6.8 match 6 stars 8.60 score 370 scripts 12 dependents

r-forge

surveillance:Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

Maintained by Sebastian Meyer. Last updated 23 hours ago.

cpp

3.6 match 2 stars 10.68 score 446 scripts 3 dependents

josiahparry

sfdep:Spatial Dependence for Simple Features

An interface to 'spdep' to integrate with 'sf' objects and the 'tidyverse'.

Maintained by Dexter Locke. Last updated 6 months ago.

r-spatialspatial

5.3 match 130 stars 7.01 score 130 scripts

covid19datahub

COVID19:COVID-19 Data Hub

Unified datasets for a better understanding of COVID-19.

Maintained by Emanuele Guidotti. Last updated 26 days ago.

2019-ncovcoronaviruscovid-19covid-datacovid19-data

3.0 match 252 stars 11.08 score 265 scripts

sbgraves237

Ecdat:Data Sets for Econometrics

Data sets for econometrics, including political science.

Maintained by Spencer Graves. Last updated 3 months ago.

3.5 match 2 stars 7.25 score 740 scripts 3 dependents

p-chevallier

htsr:Hydro-Meteorology Time-Series

Functions for the management and treatment of hydrology and meteorology time-series stored in a 'Sqlite' data base.

Maintained by Pierre Chevallier. Last updated 6 months ago.

cpp

3.3 match 4.60 score 2 scripts

pik-piam

mredgebuildings:Prepare data to be used by the EDGE-Buildings model

Prepare data to be used by the EDGE-Buildings model.

Maintained by Robin Hasse. Last updated 23 hours ago.

3.5 match 3.72 score

david-cortes

cmfrec:Collective Matrix Factorization for Recommender Systems

Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) <doi:10.1109/ICDM.2008.22>), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) <doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data.

Maintained by David Cortes. Last updated 2 months ago.

cold-startcollaborative-filteringcollective-matrix-factorizationopenblasopenmp

0.5 match 120 stars 6.84 score 23 scripts

evastrucken

lactcurves:Lactation Curve Parameter Estimation

AllCurves() runs multiple lactation curve models and extracts selection criteria for each model. This package summarises the most common lactation curve models from the last century and provides a tool for researchers to quickly decide on which model fits their data best to proceed with their analysis. Start parameters were optimized based on a dataset with 1.7 million Holstein-Friesian cows. If convergence fails, the start parameters need to be manually adjusted. The models included in the package are taken from: (1) Michaelis-Menten: Michaelis, L. and M.L. Menten (1913). <www.plantphys.info/plant_physiology/copyright/MichaelisMentenTranslation2.pdf> (1a) Michaelis-Menten (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). <doi:10.1017/S002185960007684X> (1b) Michaelis-Menten + exponential (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). <doi:10.1017/S002185960007684X> (2) Brody (1923): Brody, S., A.C. Ragsdale, and C.W. Turner (1923). <doi:10.1085/jgp.5.6.777> (3) Brody (1924): Brody, S., C.W. Tuner, and A.C. Ragsdale (1924). <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2140670/> (4) Schumacher: Schumacher, F.X. (1939) in Thornley, J.H.M. and J. France (2007). <https://books.google.com.au/books/about/Mathematical_Models_in_Agriculture.html?id=rlwBCRSHobcC&redir_esc=y> (4a) Schumacher (Lopez et al. 2015): Lopez, S. J. France, N.E. Odongo, R.A. McBride, E. Kebreab, O. AlZahal, B.W. McBride, and J. Dijkstra (2015). <doi:10.3168/jds.2014-8132> (5) Parabolic exponential (Adediran): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (6) Wood: Wood, P.D.P. (1967). <doi:10.1038/216164a0> (6a) Wood reparameterized (Dhanoa): Dhanoa, M.S. (1981). <doi:10.1017/S0003356100027276> (6b) Wood non-linear (Cappio-Borlino): Cappio-Borlino, A., G. Pulina, and G. Rossi (1995). <doi:10.1016/0921-4488(95)00713-U> (7) Quadratic Polynomial (Dave): Dave, B.K. (1971) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (8) Cobby and Le Du (Vargas): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). <doi:10.3168/jds.S0022-0302(00)75005-3> (9) Papajcsik and Bodero 1: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (10) Papajcsik and Bodero 2: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (11) Papajcsik and Bodero 3: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (12) Papajcsik and Bodero 4: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (13) Papajcsik and Bodero 6: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (14) Mixed log model 1 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (15) Mixed log model 3 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (16) Log-quadratic (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (17) Wilmink: J.B.M. Wilmink (1987). <doi:10.1016/0301-6226(87)90003-0> (17a) modified Wilmink (Jakobsen): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (17b) modified Wilmink (Laurenson & Strucken): Strucken E.M., Brockmann G.A., and Y.C.S.M. Laurenson (2019). <http://www.aaabg.org/aaabghome/AAABG23papers/35Strucken23139.pdf> (18) Bicompartemental (Ferguson and Boston 1993): Ferguson, J.D., and R. Boston (1993) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (19) Dijkstra: Dijkstra, J., J. France, M.S. Dhanoa, J.A. Maas, M.D. Hanigan, A.J. Rook, and D.E. Beever (1997). <doi:10.3168/jds.S0022-0302(97)76185-X> (20) Morant and Gnanasakthy (Pollott et al 2000): Pollott, G.E. and E. Gootwine (2000). <doi:10.1017/S1357729800055028> (21) Morant and Gnanasakthy (Vargas et al 2000): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). <doi:10.3168/jds.S0022-0302(00)75005-3> (22) Morant and Gnanasakthy (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (23) Khandekar (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (24) Ali and Schaeffer: Ali, T.E. and L.R. Schaeffer (1987). <https://www.nrcresearchpress.com/doi/pdf/10.4141/cjas87-067> (25) Fractional Polynomial (Elvira et al. 2013): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). <doi:10.1017/S175173111200239X> (26) Pollott multiplicative (Elvira): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). <doi:10.1017/S175173111200239X> (27) Pollott modified: Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (28) Monophasic Grossman: Grossman, M. and W.J. Koops (1988). <doi:10.3168/jds.S0022-0302(88)79723-4> (29) Monophasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (30) Diphasic (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (31) Diphasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (32) Legendre Polynomial (3th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (33) Legendre Polynomial (4th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (34) Legendre + Wilmink (Lidauer): Lidauer, M. and E.A. Mantysaari (1999). <https://journal.interbull.org/index.php/ib/article/view/417> (35) Natural Cubic Spline (3 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). <doi:10.3168/jds.S0022-0302(99)75277-X> (36) Natural Cubic Spline (4 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). <doi:10.3168/jds.S0022-0302(99)75277-X> (37) Natural Cubic Spline (5 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999) <doi:10.3168/jds.S0022-0302(99)75277-X> (38) Natural Cubic Spline (defined knots according to Harrell 2001): Jr. Harrell, F.E. (2001). <https://link.springer.com/book/10.1007/978-3-319-19425-7> The selection criteria measure the goodness of fit of the model and include: Residual standard error (RSE), R-square (R2), log likelihood, Akaike information criterion (AIC), Akaike information criterion corrected (AICC), Bayesian Information Criterion (BIC), Durbin Watson coefficient (DW). The following model parameters are included: Residual sum of squares (RSS), Residual standard deviation (RSD), F-value (F) based on F-ratio test.

Maintained by Eva M. Strucken. Last updated 4 years ago.

1.0 match 2.70 score 1 scripts