Showing 18 of total 18 results (show query)
hugoavmedeiros
capesR:Access to CAPES Data
Provides simplified access to the data from the Catalog of Theses and Dissertations of the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES, <https://catalogodeteses.capes.gov.br>) for the years 1987 through 2022. The dataset includes variables such as Higher Education Institution (institution), Area of Concentration (area), Graduate Program Name (program_name), Type of Work (type), Language of Work (language), Author Identification (author), Abstract (abstract), Advisor Identification (advisor), Development Region (region), State (state).
Maintained by Hugo Vasconcelos Medeiros. Last updated 3 months ago.
16.7 match 10 stars 5.18 score 2 scripts
bczernecki
thunder:Computation and Visualisation of Atmospheric Convective Parameters
Allow to compute and visualise convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly efficient engine allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational Numerical Weather Prediction models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe thunderstorms. The most relevant methods used in the package based on the following publications Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>, McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>, Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>, Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>, Showalter (1953) <doi:10.1175/1520-0477-34.6.250>, Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>, Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>, Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>, Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>, Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>, Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.
Maintained by Bartosz Czernecki. Last updated 12 months ago.
capecinconvective-parametersdownload-soundinghodographrawinsondesevere-weatherthundertornadocpp
11.0 match 45 stars 6.31 score 7 scripts
wjbraun
DAAG:Data Analysis and Graphics Data and Functions
Functions and data sets used in examples and exercises in the text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data Analysis and Graphics Using R", and in an upcoming Maindonald, Braun, and Andrews text that builds on this earlier text.
Maintained by W. John Braun. Last updated 11 months ago.
7.3 match 8.25 score 1.2k scripts 1 dependents
leobiazoli
capesData:Data on Scholarships in CAPES International Mobility Programs
Information on activities to promote scholarships in Brazil and abroad for international mobility programs, recorded in Capes' computerized payment systems. The CAPES database refers to international mobility programs for the period from 2010 to 2019 <https://dadosabertos.capes.gov.br/dataset/>.
Maintained by Leonardo Biazoli. Last updated 6 months ago.
10.9 match 1 stars 3.54 score 2 scripts
mages
ChainLadder:Statistical Methods and Models for Claims Reserving in General Insurance
Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.
Maintained by Markus Gesmann. Last updated 1 months ago.
3.1 match 82 stars 10.04 score 196 scripts 2 dependents
rsetienne
DAISIE:Dynamical Assembly of Islands by Speciation, Immigration and Extinction
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. (2015) <doi:10.1111/ele.12461>.
Maintained by Rampal S. Etienne. Last updated 1 months ago.
3.2 match 9 stars 8.59 score 55 scripts 1 dependents
hturner
BradleyTerry2:Bradley-Terry Models
Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
Maintained by Heather Turner. Last updated 6 years ago.
bradley-terry-modelspaired-comparisonsstatistical-models
3.4 match 20 stars 7.97 score 172 scripts 1 dependents
justinmshea
neverhpfilter:An Alternative to the Hodrick-Prescott Filter
In the working paper titled "Why You Should Never Use the Hodrick-Prescott Filter", James D. Hamilton proposes a new alternative to economic time series filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.
Maintained by Justin M. Shea. Last updated 2 years ago.
3.5 match 14 stars 5.93 score 61 scripts
pet221
SSNbler:Assemble 'SSN' Objects
Import, create and assemble data needed to fit spatial-statistical stream-network models using the 'SSN2' package for 'R'. Streams, observations, and prediction locations are represented as simple features and specific tools provided to define topological relationships between features; calculate the hydrologic distances (with flow-direction preserved) and the spatial additive function used to weight converging stream segments; and export the topological, spatial, and attribute information to an `SSN` (spatial stream network) object, which can be efficiently stored, accessed and analysed in 'R'. A detailed description of methods used to calculate and format the spatial data can be found in Peterson, E.E. and Ver Hoef, J.M., (2014) <doi:10.18637/jss.v056.i02>.
Maintained by Erin Peterson. Last updated 6 months ago.
3.3 match 10 stars 6.01 score 17 scripts
ldbk
m2b:Movement to Behaviour Inference using Random Forest
Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.
Maintained by Laurent Dubroca. Last updated 8 years ago.
3.2 match 2 stars 4.08 score 12 scripts
friendly
VisCollin:Visualizing Collinearity Diagnostics
Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Where’s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>. These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.
Maintained by Michael Friendly. Last updated 1 years ago.
biplotscollinearity-diagnosticsgraphicsregression-models
3.6 match 1 stars 2.78 score 12 scripts
cran
TideHarmonics:Harmonic Analysis of Tides
Implements harmonic analysis of tidal and sea-level data. Over 400 harmonic tidal constituents can be estimated, all with daily nodal corrections. Time-varying mean sea-levels can also be used.
Maintained by Alec Stephenson. Last updated 8 years ago.
3.6 match 2.65 score 15 scripts 1 dependents
slihn
jubilee:Forecasting Long-Term Growth of the U.S. Stock Market and Business Cycles
A long-term forecast model called "Jubilee-Tectonic model" is implemented to forecast future returns of the U.S. stock market, Treasury yield, and gold price. The five-factor model forecasts the 10-year and 20-year future equity returns with high R-squared above 80 percent. It is based on linear growth and mean reversion characteristics in the U.S. stock market. This model also enhances the CAPE model by introducing the hypothesis that there are fault lines in the historical CAPE, which can be calibrated and corrected through statistical learning. In addition, it contains a module for business cycles, optimal interest rate, and recession forecasts.
Maintained by Stephen H-T. Lihn. Last updated 5 years ago.
2.5 match 2.66 score 23 scripts
djpedregal
UComp:Automatic Univariate Time Series Modelling of many Kinds
Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) <doi:10.1017/CBO9781107049994>. Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>. Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>. Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>. Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>. Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.
Maintained by Diego J. Pedregal. Last updated 18 days ago.
3.6 match 1 stars 1.70 score 1 scriptscran
aiRthermo:Atmospheric Thermodynamics and Visualization
Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.
Maintained by Santos J. González-Rojí. Last updated 7 years ago.
2.0 match 1.48 score 1 dependentscran
SuessR:Suess and Laws Corrections for Marine Stable Carbon Isotope Data
Generates region-specific Suess and Laws corrections for stable carbon isotope data from marine organisms collected between 1850 and 2023. Version 0.1.6 of 'SuessR' contains four built-in regions: the Bering Sea ('Bering Sea'), the Aleutian archipelago ('Aleutian Islands'), the Gulf of Alaska ('Gulf of Alaska'), and the subpolar North Atlantic ('Subpolar North Atlantic'). Users can supply their own environmental data for regions currently not built into the package to generate corrections for those regions.
Maintained by Casey Clark. Last updated 26 days ago.
1.6 match 1.00 score 2 scripts