Found 514 packages in 0.01 seconds
Price Index Aggregation
Most price indexes are made with a two-step procedure, where
period-over-period elemental indexes are first calculated for a collection
of elemental aggregates at each point in time, and then aggregated according
to a price index aggregation structure. These indexes can then be chained
together to form a time series that gives the evolution of prices with
respect to a fixed base period. This package contains a collection of
functions that revolve around this work flow, making it easy to build
standard price indexes, and implement the methods described by
Balk (2008,
Objective General Index
Consider a data matrix of n individuals with p variates. The objective general index (OGI)
is a general index that combines the p variates into a univariate index in order to rank the n
individuals. The OGI is always positively correlated with each of the variates.
More details can be found in Sei (2016)
Binary Indexed Tree
A simple implementation of Binary Indexed Tree by R. The BinaryIndexedTree class supports construction of Binary Indexed Tree from a vector, update of a value in the vector and query for the sum of a interval of the vector.
Count and Continuous Generalized Variability Indexes
Firstly, both functions of the univariate Poisson dispersion index (DI) for count data and the univariate exponential variation index (VI) for nonnegative continuous data are performed. Next, other functions of univariate indexes such the binomial dispersion index (DIb), the negative binomial dispersion index (DInb) and the inverse Gaussian variation index (VIiG) are given. Finally, we are computed some multivariate versions of these functions such that the generalized dispersion index (GDI) with its marginal one (MDI) and the generalized variation index (GVI) with its marginal one (MVI) too.
Multilevel Index of Dissimilarity
Tools and functions to fit a multilevel index of dissimilarity.
Memory-Efficient Storage of Large Data on Disk and Fast Access Functions
The ff package provides data structures that are stored on disk but behave (almost) as if they were in RAM by transparently mapping only a section (pagesize) in main memory - the effective virtual memory consumption per ff object. ff supports R's standard atomic data types 'double', 'logical', 'raw' and 'integer' and non-standard atomic types boolean (1 bit), quad (2 bit unsigned), nibble (4 bit unsigned), byte (1 byte signed with NAs), ubyte (1 byte unsigned), short (2 byte signed with NAs), ushort (2 byte unsigned), single (4 byte float with NAs). For example 'quad' allows efficient storage of genomic data as an 'A','T','G','C' factor. The unsigned types support 'circular' arithmetic. There is also support for close-to-atomic types 'factor', 'ordered', 'POSIXct', 'Date' and custom close-to-atomic types. ff not only has native C-support for vectors, matrices and arrays with flexible dimorder (major column-order, major row-order and generalizations for arrays). There is also a ffdf class not unlike data.frames and import/export filters for csv files. ff objects store raw data in binary flat files in native encoding, and complement this with metadata stored in R as physical and virtual attributes. ff objects have well-defined hybrid copying semantics, which gives rise to certain performance improvements through virtualization. ff objects can be stored and reopened across R sessions. ff files can be shared by multiple ff R objects (using different data en/de-coding schemes) in the same process or from multiple R processes to exploit parallelism. A wide choice of finalizer options allows to work with 'permanent' files as well as creating/removing 'temporary' ff files completely transparent to the user. On certain OS/Filesystem combinations, creating the ff files works without notable delay thanks to using sparse file allocation. Several access optimization techniques such as Hybrid Index Preprocessing and Virtualization are implemented to achieve good performance even with large datasets, for example virtual matrix transpose without touching a single byte on disk. Further, to reduce disk I/O, 'logicals' and non-standard data types get stored native and compact on binary flat files i.e. logicals take up exactly 2 bits to represent TRUE, FALSE and NA. Beyond basic access functions, the ff package also provides compatibility functions that facilitate writing code for ff and ram objects and support for batch processing on ff objects (e.g. as.ram, as.ff, ffapply). ff interfaces closely with functionality from package 'bit': chunked looping, fast bit operations and coercions between different objects that can store subscript information ('bit', 'bitwhich', ff 'boolean', ri range index, hi hybrid index). This allows to work interactively with selections of large datasets and quickly modify selection criteria. Further high-performance enhancements can be made available upon request.
Half-Weight Index Gregariousness
The half-weight index gregariousness (HWIG) is an association
index used in social network analyses. It extends the half-weight
association index (HWI), correcting for level of gregariousness
in individuals. It is calculated using group by individual
data according to methods described in Godde et al. (2013)
The Bimodality Index
Defines the functions used to compute the
bimodal index as defined by Wang et al. (2009)
< https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730180/>,
Constrained Single Index Model Estimation
Estimation of function and index vector in single index model with and without shape constraints including different smoothness conditions.
Outside Option Index
Calculates the Outside Option Index proposed by Caldwell and Danieli (2018) < https://drive.google.com/file/d/1j-uwD19S4gqgXIXeYch9jGBCaDhWZlRQ/view>. This index uses the cross- sectional concentration of similar workers across job types to quantify the availability of outside options as a function of workers’ characteristics (e.g. commuting costs, preferences, and skills.)