A library of R utility functions that I find useful for my work on:
- customer cohort analysis
- age-period-cohort analysis
- customer-based firm valuations
- …
Includes utility functions that are useful for when one needs to work with both Excel and R interchangeably.
The library includes many functions for creating cohort matrices from scratch.
GenConstantValues(value = 10, dim = 5)## [,1] [,2] [,3] [,4] [,5]
## [1,] 10 10 10 10 10
## [2,] NA 10 10 10 10
## [3,] NA NA 10 10 10
## [4,] NA NA NA 10 10
## [5,] NA NA NA NA 10
GenDiagonalValues(value = 10, dim = 5)## [,1] [,2] [,3] [,4] [,5]
## [1,] 10 0 0 0 0
## [2,] 0 10 0 0 0
## [3,] 0 0 10 0 0
## [4,] 0 0 0 10 0
## [5,] 0 0 0 0 10
GenExpGrowthOverTime(factor = 0.9, dim = 5)## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 0.9 0.81 0.729 0.6561
## [2,] NA 1.0 0.90 0.810 0.7290
## [3,] NA NA 1.00 0.900 0.8100
## [4,] NA NA NA 1.000 0.9000
## [5,] NA NA NA NA 1.0000
GenExpGrowthOverCohorts(factor = 1.1, dim = 5)## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 0.0 0.00 0.000 0.0000
## [2,] 0 1.1 0.00 0.000 0.0000
## [3,] 0 0.0 1.21 0.000 0.0000
## [4,] 0 0.0 0.00 1.331 0.0000
## [5,] 0 0.0 0.00 0.000 1.4641
row <- c(0.9, 0.91, 0.92, 0.93, 0.94)
GenMatrixFromRow(row = row)## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9 0.91 0.92 0.93 0.94
## [2,] NA 0.90 0.91 0.92 0.93
## [3,] NA NA 0.90 0.91 0.92
## [4,] NA NA NA 0.90 0.91
## [5,] NA NA NA NA 0.90
# From period-by-period customer retention rates to survival rates
retentionRates <- c(1, 0.9, 0.8, 0.7, 0.6)
retentionMatrix <- GenMatrixFromRow(row = retentionRates)
survivalRates <- TransformRetentionRates(
X = retentionMatrix,
from = "period-by-period",
to = "survival"
)
survivalRates## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 0.9 0.72 0.504 0.3024
## [2,] NA 1.0 0.90 0.720 0.5040
## [3,] NA NA 1.00 0.900 0.7200
## [4,] NA NA NA 1.000 0.9000
## [5,] NA NA NA NA 1.0000
numAcquiredCustomers <- c(100, 120, 140, 160, 180)
GenCustMatrixFromAcquisitions(
nAcquired = numAcquiredCustomers,
survMatrix = survivalRates
)## [,1] [,2] [,3] [,4] [,5]
## [1,] 100 90 72 50.4 30.24
## [2,] NA 120 108 86.4 NA
## [3,] NA NA 140 NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
ageEffects <- c(10, 20, 30, 40, 50)
periodEffects <- c(0, 0, 0, 0, 5)
cohortEffects <- c(0, -2, -4, -6, -8)
intercept <- 0.1
# y_ijk = intercept + age_i + period_j + cohort_k
GenAPCMatrix(
constant = intercept,
ageEffects = ageEffects,
periodEffects = periodEffects,
cohortEffects = cohortEffects
)## [,1] [,2] [,3] [,4] [,5]
## [1,] 10.1 20.1 30.1 40.1 55.1
## [2,] NA 8.1 18.1 28.1 43.1
## [3,] NA NA 6.1 16.1 31.1
## [4,] NA NA NA 4.1 19.1
## [5,] NA NA NA NA 7.1