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Nonlinear nonparametric statistics using partial moments

Project description

NNS-Python

Nonlinear Nonparametric Statistics

Install

pip install NNS

Implemented Functions:

From Beta R Version of 2021-12-13 (Version: 8.4-Beta, Date: 2021-12-13)

  • ANOVA

    • NNS.ANOVA: TODO (deps: NNS.ANOVA.bin)
  • ARMA

    • NNS.ARMA: TODO (deps: NNS.seas, ARMA.seas.weighting, NNS.meboot)
  • ARMA_optim:

    • NNS.ARMA.optim: TODO (deps: NNS.ARMA)
  • Binary_ANOVA

    • NNS.ANOVA.bin: OK
  • Boost

    • NNS.boost: TODO (deps: NNS.caus, NNS.reg, NNS.stack)
  • Causal_Matrix

    • NNS.caus.matrix: TODO (deps: NNS.caus)
  • Causation

    • NNS.caus: TODO (deps: Uni.caus, NNS.caus.matrix)
  • Copula

    • NNS.copula: OK
  • Dependence

    • NNS.dep: TODO (deps: NNS.part, NNS.dep.matrix)
  • Dependence_matrix

    • NNS.dep.matrix: TODO (deps: NNS.dep)
  • dy_d_wrt

    • dy.d_: TODO (deps: NNS.reg)
  • dy_dx

    • dy_dx: TODO (deps: NNS.dep, NNS.reg)
  • Internal Functions

    • mode: TEST
    • mode_class: TODO
    • gravity: TEST
    • gravity_class: TODO
    • factor_2_dummy: TODO
    • factor_2_dummy_FR: TODO
    • generate_vectors: TODO
    • ARMA_seas_weighting: TODO
    • is.discrete: TODO
    • lag_mtx: TODO
    • NNS_meboot_part: TODO
    • NNS_meboot_expand_sd: TODO
    • alt_cbind: TEST (not in newest version, maybe R related)
    • RP: TODO (not in newest version)
  • LPM UPM VaR

    • LPM_VaR: OK
    • UPM_VaR: OK
    • used np.quantile instead of tdigest, and root_scalar instead of optimize
  • Multivariate_Regression

    • NNS.M.reg: TODO (deps: NNS.part, NNS.dep, NNS::NNS.distance, NNS.copula, NNS.reg)
  • NNS_Distance

    • NNS.distance: TODO (deps: dtw, Rfast)
  • NNS_meboot

    • NNS.meboot: TODO (deps: NNS.dep, NNS.meboot.expand.sd)
  • NNS_term_matrix

    • NNS.term.matrix: OK
  • NNS_VAR

    • NNS.VAR: TODO (deps: NNS.reg, NNS.seas, NNS.ARMA.optim, NNS.ARMA, NNS.stack, NNS.dep, NNS.caus)
  • Normalization

    • NNS.norm: TODO (deps: NNS.dep, Rfast)
  • Nowcast

    • NNS.nowcast: TODO (deps: Quandl, NNS.VAR)
  • Numerical Differentiation

    • NNS.diff: TODO (nodeps)
  • Partition_Map

    • NNS.part: TODO (deps: internal functions: gravity_class, gravity, mode_class)
  • Partial Moments

    • pd_fill_diagonal: OK (Internal use)
    • LPM: OK Tested
      • numba_LPM: Numba version (Internal use)
      • LPM: Vectorized / pandas / numpy friendly
    • UPM: OK Tested
      • numba_UPM: Numba version (Internal use)
      • UPM: Vectorized / pandas / numpy friendly
    • Co_UPM: OK Tested
      • _Co_UPM: Internal Use
      • _vec_Co_UPM: numpy.vectorized
      • Co_UPM: Vectorized / pandas / numpy friendly
    • Co_LPM: OK Tested
      • _Co_LPM: Internal Use
      • _vec_Co_LPM: numpy.vectorized
      • Co_LPM: Vectorized / pandas / numpy friendly
    • D_LPM: OK Tested
      • _D_LPM: Internal User
      • _vec_D_LPM: numpy.vectorized
      • D_LPM: Vectorized / pandas / numpy friendly
    • D_UPM: OK Tested
      • _D_UPM: Internal User
      • _vec_D_UPM: numpy.vectorized
      • D_UPM: Vectorized / pandas / numpy friendly
    • PM_matrix: OK
    • LPM_ratio: OK
    • UPM_ratio: OK
    • NNS_PDF: TODO (deps: d/dx approximation, density)
    • NNS_CDF: TODO (deps: ecdf, density, matplotlib, NNS_reg)
  • Regression

    • NNS.reg: TODO (deps: NNS.M.reg, NNS.dep, NNS.part, Uni.caus)
  • SD Efficient Set

    • NNS_SD_efficient_set: OK (TODO: numba version?)
  • Seasonality_Test

    • NNS.seas: TODO (nodeps)
  • Stack

    • NNS.stack: TODO (deps: NNS.reg, NNS::NNS.distance)
  • Uni_Causation

    • Uni.caus: TODO (deps: NNS.norm, NNS.dep)
  • FSD, SSD, TSD

    • NNS_FSD: OK (TODO: numba version?)
    • NNS_SSD: OK (TODO: numba version?)
    • NNS_TSD: OK (TODO: numba version?)
  • Uni SD Routines

    • NNS_FSD_uni: OK (TODO: numba version?)
    • NNS_SSD_uni: OK (TODO: numba version?)
    • NNS_TSD_uni: OK (TODO: numba version?)
  • Others Todos:

    • Try to make names equal to R version
      • R accept $ and . we will replace to underline _
      • TODO: R export functions from modules

Project details


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